Korte Spider-Manfilm over mentale gezondheid woensdag ...

&quot;Spider-Verse&quot; animated short &quot;The Spider Within&quot; will launch on YouTube from Sony Pictures Animation and Imageworks on March 26.

'Spider-Verse' Animated Short 'The Spider Within' Launches on YouTube

Reddit en YouTube komen in de VS voor de rechter ...

A 25-year-old farmer with more than 200,000 subscribers to her YouTube channel says more than half of her audience is female as she films her days on a sheep and cropping property in south-west Victoria.

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YouTube is introducing the ability for users to incorporate or &quot;remix&quot; a music video in their short-form videos, called Shorts, as the company continues to challenge TikTok. Given that YouTube has something that TikTok doesn&#x27;t, which is a vast library of official music videos, it makes sense ...

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A New York state judge ruled that plaintiffs may try to prove social media platforms were designed to addict and radicalize users like the shooter, who was 18 at the time.

Judge rules Reddit and YouTube must face lawsuits claiming they enabled Buffalo grocery mass shooter

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The new rules require YouTube uploaders to disclose when their videos contain AI-generated effects, like voice cloning or face swapping. The videos will then receive a label.

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Beloved BC comic Ivan Decker is set to release his latest comedy special, POPCORN, for free on YouTube tonight · The show, originally presented by 800 Pound Gorilla on February 13, is a standup special that details anecdotes from Decker’s life—from navigating marriage and fatherhood to ...

Local comic Ivan Decker releasing “POPCORN” special on YouTube |

Reddit and Google&#x27;s YouTube were ordered by a New York state judge to face lawsuits seeking to hold them responsible for helping enable the avowed white supremacist who killed 10 Black people in 2022 at a Buffalo, New York grocery store.

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HackerNoon Reporter: Please tell us briefly about your background. Hi, I’m Jason! I grew up between Boston and Nashville. I then spent 10 years in Washington DC, first attending The American University studying International Relations, then 7 years as a consultant for USAID, World Bank, and others. I started spending time in East Africa through that work in 2010 and moved to Kenya in 2013 to start MARAMOJA, the first taxi app anywhere in Africa at the time. We realized in the process of building that taxi app that we were solving the wrong problem. We realized the problem was actually about trust. Then we began to focus our attention there, building better models of digital trust. What's your startup called? And in a sentence or two, what does it do? Our startup is called UTU, which means “humanity” in Swahili. UTU is building the trust infrastructure of the internet to help businesses and consumers engage and transact in an easier, safer, and more trustworthy way. Our AI-based API products collect and analyze data to create trust signals and personalized recommendations that help consumers and businesses make the best decisions for their situation. And the UTU blockchain protocol rewards users for trustworthy actions and compensates them for sharing their data while protecting their privacy. UTU changes the economics of trust, ensures trust can’t be bought or manipulated and leverages data to help people make better decisions. What is the origin story? As I mentioned earlier, UTU spawned from our work on MARAMOJA. In other more developed metropolitan areas, like New York and London, drivers and riders are interchangeable commodities. But in Kenya, and indeed in many parts of the world, trust is the key to how riders select drivers, and vice versa, and we realized this when building MARAMOJA. So we began working on new models of digital trust to support this. We realized that current online trust systems can’t always be trusted and are out of date. Anonymous ratings, reviews, and trust scores are often manipulated, static, and don’t reflect how we trust in the real world, which is based on relationships and connections. These issues lead to poor purchase decisions for consumers, and harmed reputations, and lost sales for online businesses. We also saw that artificial intelligence can be used to provide personalized recommendations based on users’ own networks and individualized models of trust. Even our earliest, simplest implementations of UTU’s concepts yielded +20% conversion rates and customer satisfaction, along with higher retention rates and lifetime value. People started reading about our approach to trust and coming to us from all over the world, telling us about the trust problems of their marketplaces, and asking to license our technology. This was the genesis of UTU. We later saw that blockchain can be leveraged to ensure that these ratings and reviews can’t be manipulated and that a token model could be layered in to reinforce our product value proposition with an economic incentive model that promotes digital trust. What do you love about your team, and why are you the ones to solve this problem? We’ve got both practical and theoretical knowledge and experience in trust. As mentioned, we first built the trust mechanism from a need that we encountered with MARAMOJA, so this is not just some theoretical game for us. Our CTO Bastian Blankenburg has also built and used trust models in his own research, and we’re collaborating with the University of Southampton in the UK in this respect. We have some of the strongest AI engineers and data scientists (many PhDs) on our engineering team. On the business side, we have serial entrepreneurs on our management team who have built, launched, and grown multiple companies. And we are extremely well connected on the African continent, which is a massive emerging market for crypto and faces many of the problems we are solving. If you weren’t building your startup, what would you be doing? I always struggle with this question. I’m building what I know to be my life’s work and there’s nothing else I should be doing other than this. When you get to wake up every day and try to move the world closer to a model of digital trust that actually works for people and improves our online experiences, there’s little cause or time to think about what else I might do. At the moment, how do you measure success? What are your core metrics? The metric we are optimizing for is the number of API calls for our products, which is a function of how many companies are using our APIs. So we are actively marketing and selling our Trust API to online marketplace companies, as well as our Creditworthiness API to financial services firms and decentralized finance (DeFi) lending platforms. What’s most exciting about your traction to date? The level of enthusiasm for what we’re building has been amazing. We’ve been able to garner about 15 pilot clients in a short amount of time. And what’s so interesting is that our solutions can be applied to so many different industries and use cases, and this range is reflected in our client base already. So we’re really excited to see how far-reaching we can be when we hit scale. What technologies are you currently most excited about, and most worried about? And why? I am most excited about the two technologies we’re working with today - blockchain and artificial intelligence. Within those, I’d say I’m excited by any technology that embraces our humanity, rather than seeking to replace it. Blockchain is able to transform the economics of products and give users and communities ownership in the platforms that they use. This is significantly changing how business is done today.  And the immutability of the data on the blockchain ensures that data, like ratings and reviews, can’t be manipulated. AI is another technology that is transformative. The ability to analyze and glean insights from massive amounts of data relatively quickly is amazing, and our personalized recommendations wouldn’t be possible without it. It’s ironic that one of the technologies that I am most worried about is also AI. We’ve heard all the stories about facial recognition and surveillance that are used to intrude on privacy, and this is all facilitated by AI. That’s why we are so adamant in ensuring that the data that we collect is done so with the permission of our users and that data is used in a privacy-preserving manner. What drew you to get published on HackerNoon? What do you like most about our platform? I’ve been reading Hacker Noon for many years now, and I admire how it went from being a developer-focused blog to a massive, wide-ranging tech publication. The content ranges from developer tutorials to life-improvement stories all focused on tech. It’s truly a unique reading experience, and we would be honored to be published on Hacker Noon. What advice would you give to the 21-year-old version of yourself? I’d probably tell myself to stop talking and listen. I’d also tell myself to stop trying to plan a career and start pursuing whichever things fascinate me at the time, and trust that all the dots will connect somehow. What is something surprising you've learned this year that your contemporaries would benefit from knowing? This year I’ve learned a tremendous amount about mental wellness and resilience. I’ve had a long and winding journey, with many challenges, professional and personal along the way. As an entrepreneur, our teams draw from our energy - staying mentally strong through the ups and downs of a startup is critical for both personal wellbeing and company success. I do this through a combination of meditation, yoga, and breathwork that help me stay centered and focused. Vote for UTU as . the startup of the year, Nairobi, Kenya

'UTU Changes the Economics of Trust', says Jason Eisen, CEO UTU

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10 raisons pour lesquelles publier sur HackerNoon augmentera considérablement votre portée et votre impact

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Table of Links Abstract and 1 Introduction Methodology
Experiments
Conclusion and References 4. CONCLUSION In this paper, we propose the framework of Deep Mutual Learning across task towers(DML), which is compatible with various backbone multi-task networks. Extensive offline experiments help to verify DML’s effectiveness on multiple real-world datasets and across various base models. Moreover, thorough ablation studies are carried out to verify and understand the value of each newly introduced module. Finally, DML achieves significant online gains and has already been deployed to the online platform. REFERENCES [1] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning.. In Osdi, Vol. 16. Savannah, GA, USA, 265–283. [2] Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee. 2018. Understanding Deep Neural Networks with Rectified Linear Units. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=B1J_rgWRW [3] Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997), 41–75. [4] Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198. [5] Ke Ding, Xin Dong, Yong He, Lei Cheng, Chilin Fu, Zhaoxin Huan, Hai Li, Tan Yan, Liang Zhang, Xiaolu Zhang, et al. 2021. MSSM: a multiple-level sparse sharing model for efficient multi-task learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2237–2241. [6] Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 2: short papers). 845–850. [7] Peter A Flach, José Hernández-Orallo, and Cèsar Ferri Ramirez. 2011. A coherent interpretation of AUC as a measure of aggregated classification performance. In ICML. [8] F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1–19. [9] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation 3, 1 (1991), 79–87. [10] Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980 [11] Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, and Ed H Chi. 2019. Snr: Sub-network routing for flexible parameter sharing in multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 216–223. [12] Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-ofexperts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1930–1939. [13] Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1137–1140. [14] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43–52. [15] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016. Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3994–4003. [16] Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2019. Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4822–4829. [17] Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems. 269– 278. [18] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). [19] Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu. 2013. A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th annual conference on learning theory (COLT 2013), Vol. 8. 6. [20] Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Li Wei, and Ed H Chi. 2022. Can Small Heads Help? Understanding and Improving Multi-Task Generalization. In Proceedings of the ACM Web Conference 2022. 3009– 3019. [21] Wikipedia. 2023. Pearson correlation coefficient — Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Pearson%20correlation% 20coefficient&oldid=1146097966. [Online; accessed 15-April-2023]. [22] Wikipedia contributors. 2022. Evaluation measures (information retrieval) — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title= Evaluation_measures_(information_retrieval)&oldid=1095286224. [Online; accessed 9-January-2023]. [23] Wikipedia contributors. 2022. Mean squared error — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Mean_squared_error& oldid=1127519968. [Online; accessed 31-January-2023]. [24] Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021. Modeling the sequential dependence among audience multistep conversions with multi-task learning in targeted display advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3745–3755. [25] Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering (2021). [26] Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: a multitask ranking system. In Proceedings of the 13th ACM Conference on Recommender Systems. 43–51. Authors:
(1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com);
(2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com);
(3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com);
(4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). This paper is available on arxiv under CC BY 4.0 DEED license. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Methodology Experiments Conclusion and References Methodology Methodology Experiments Experiments Conclusion and References Conclusion and References 4. CONCLUSION In this paper, we propose the framework of D eep M utual L earning across task towers( DML ), which is compatible with various backbone multi-task networks. Extensive offline experiments help to verify DML’s effectiveness on multiple real-world datasets and across various base models. Moreover, thorough ablation studies are carried out to verify and understand the value of each newly introduced module. Finally, DML achieves significant online gains and has already been deployed to the online platform. D M L DML REFERENCES [1] Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: a system for large-scale machine learning.. In Osdi, Vol. 16. Savannah, GA, USA, 265–283. [2] Raman Arora, Amitabh Basu, Poorya Mianjy, and Anirbit Mukherjee. 2018. Understanding Deep Neural Networks with Rectified Linear Units. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=B1J_rgWRW [3] Rich Caruana. 1997. Multitask learning. Machine learning 28, 1 (1997), 41–75. [4] Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep neural networks for youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems. 191–198. [5] Ke Ding, Xin Dong, Yong He, Lei Cheng, Chilin Fu, Zhaoxin Huan, Hai Li, Tan Yan, Liang Zhang, Xiaolu Zhang, et al. 2021. MSSM: a multiple-level sparse sharing model for efficient multi-task learning. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2237–2241. [6] Long Duong, Trevor Cohn, Steven Bird, and Paul Cook. 2015. Low resource dependency parsing: Cross-lingual parameter sharing in a neural network parser. In Proceedings of the 53rd annual meeting of the Association for Computational Linguistics and the 7th international joint conference on natural language processing (volume 2: short papers). 845–850. [7] Peter A Flach, José Hernández-Orallo, and Cèsar Ferri Ramirez. 2011. A coherent interpretation of AUC as a measure of aggregated classification performance. In ICML. [8] F Maxwell Harper and Joseph A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1–19. [9] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. 1991. Adaptive mixtures of local experts. Neural computation 3, 1 (1991), 79–87. [10] Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1412.6980 [11] Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, and Ed H Chi. 2019. Snr: Sub-network routing for flexible parameter sharing in multi-task learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 216–223. [12] Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. 2018. Modeling task relationships in multi-task learning with multi-gate mixture-ofexperts. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1930–1939. [13] Xiao Ma, Liqin Zhao, Guan Huang, Zhi Wang, Zelin Hu, Xiaoqiang Zhu, and Kun Gai. 2018. Entire space multi-task model: An effective approach for estimating post-click conversion rate. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval. 1137–1140. [14] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. 43–52. [15] Ishan Misra, Abhinav Shrivastava, Abhinav Gupta, and Martial Hebert. 2016. Cross-stitch networks for multi-task learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3994–4003. [16] Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2019. Latent multi-task architecture learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4822–4829. [17] Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. 2020. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Fourteenth ACM Conference on Recommender Systems. 269– 278. [18] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017). [19] Yining Wang, Liwei Wang, Yuanzhi Li, Di He, Wei Chen, and Tie-Yan Liu. 2013. A theoretical analysis of NDCG ranking measures. In Proceedings of the 26th annual conference on learning theory (COLT 2013), Vol. 8. 6. [20] Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Li Wei, and Ed H Chi. 2022. Can Small Heads Help? Understanding and Improving Multi-Task Generalization. In Proceedings of the ACM Web Conference 2022. 3009– 3019. [21] Wikipedia. 2023. Pearson correlation coefficient — Wikipedia, The Free Encyclopedia. http://en.wikipedia.org/w/index.php?title=Pearson%20correlation% 20coefficient&oldid=1146097966. [Online; accessed 15-April-2023]. [22] Wikipedia contributors. 2022. Evaluation measures (information retrieval) — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title= Evaluation_measures_(information_retrieval)&oldid=1095286224. [Online; accessed 9-January-2023]. [23] Wikipedia contributors. 2022. Mean squared error — Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Mean_squared_error& oldid=1127519968. [Online; accessed 31-January-2023]. [24] Dongbo Xi, Zhen Chen, Peng Yan, Yinger Zhang, Yongchun Zhu, Fuzhen Zhuang, and Yu Chen. 2021. Modeling the sequential dependence among audience multistep conversions with multi-task learning in targeted display advertising. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3745–3755. [25] Yu Zhang and Qiang Yang. 2021. A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering (2021). [26] Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending what video to watch next: a multitask ranking system. In Proceedings of the 13th ACM Conference on Recommender Systems. 43–51. Authors: (1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com); (2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com); (3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com); (4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). Authors: Authors: (1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com); (2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com); (3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com); (4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

DML Revolutionizes Multi-Task Learning with Proven Effectiveness and Real-World Deployment

Authors:
(1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com);
(2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com);
(3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com);
(4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). Table of Links Abstract and 1 Introduction Methodology
Experiments
Conclusion and References ABSTRACT Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data. The typical way of conducting multi-task learning(MTL) is to establish appropriate parameter sharing across multiple tasks at lower layers while reserving a separate task tower for each task at upper layers. With such design, the lower layers intend to explore the structure of task relationships and mine valuable information to be used by the task towers for accurate prediction. Since the task towers exert direct impact on the prediction results, we argue that the architecture of standalone task towers is suboptimal for promoting positive knowledge sharing. First, for each task, attending to the input information of other task towers is beneficial. For instance, the information useful for predicting the "like" task is also valuable for the "buy" task. Furthermore, because different tasks are inter-related, the training labels of multiple tasks should obey a joint distribution. It is undesirable for the prediction results for these tasks to fall into the low density areas. Accordingly, we propose the framework of Deep Mutual Learning across task towers(DML), which is compatible with various backbone multitask networks. At the entry layer of the task towers, the shared component of Cross Task Feature Mining(CTFM) is introduced to transfer input information across the task towers while still ensuring one task’s loss will not impact the inputs of other task towers. Moreover, for each task, dedicated network component called Global Knowledge Distillation(GKD) are utilized to distill valuable knowledge from the global results of the upper layer task towers to enhance the prediction consistency. Extensive offline experiments and online A/B tests are conducted to evaluate and verify the proposed approach’s effectiveness. 1. INTRODUCTION Recently, we have seen the widespread application of recommender systems, which involve different types of user feedback signals, such as clicking, rating, commenting, etc. Moreover, no single feedback signal can accurately reflect user satisfaction. For example, over-concentrating on clicking may aggravate the click-bait issue. Therefore, it is highly desirable to be able to effectively learn and estimate multiple types of user behaviors at the same time. And Multi-Task Learning is a promising technique to address this challenge. Given several related learning tasks, the goal of multi-task learning is to enhance the overall performance of different tasks by leveraging knowledge transfer among tasks. With the Multi-Task Learning(MTL) paradigm[3, 25], multiple tasks are learned simultaneously in a single model. Compared with single-task solutions, MTL costs much fewer machine resources and improves learning efficiency since we just need to train and deploy a single model. Moreover, MTL usually can warrant enhanced recommendation performance through appropriate parameter sharing. Most existing methods of Multi-Task Learning(MTL) appropriately share parameters across multiple tasks at lower layers while keeping separate task towers at upper layers. These methods can be roughly classified into four categories. The first category comprises the methods of hard parameter sharing. Among them, embedding sharing is the most intuitive structure to share information. For instance, the ESSM model [13] shares embedding parameters between the tasks of CTR (Click-Through Rate) and CVR (Conversion Rate) for improving the prediction performance of the sparse CVR task. In addition to the embedding parameters, the Shared-Bottom structure[3] is introduced to share the parameters of lower-layer MLPs among tasks. But these methods are severely plagued by the task conflicts and negative transfer issue. Second, for the methods of soft parameter sharing, each task owns separate parameters, which are regularized during training to minimize the differences between the shared parameters. L2-constrained[6] is a typical algorithm belonging to this category. Third, for the methods of customized routing, they learn customized routing weights for each task to combine and fuse information from lower-layer networks to counteract the negative transfer issue. Cross-stitch network[15] and sluice network[16] learn separate linear weights for each task to selectively merge representations from different lower-level branches. SNR[11] modularizes the shared low-level layers into parallel sub-networks and uses a transformation matrix multiplied by a scalar coding variable to learn their connections to upper-level layers to alleviate the task conflict and negative transfer issue. MSSM[5] learns differing combinations of feature fields for each expert and designs finer-grained sharing patterns among tasks through a set of coding variables that selectively choose which cells to route for a given task. But the learned routing parameters of these methods are static for all the samples, which can hardly warrant optimal performance. Finally, the methods of dynamic gating learn optimized weights for each task based on the input sample to effectively combine the outputs of lower-level networks and achieve success in industrial applications. The MMoE [12] model adapts the Mixture-of-Experts (MoE)[9] structure to multi-task learning by sharing the expert sub-networks across all tasks, while also maintaining separate gating network optimized for each task. And Zhao et al. [26] extend the MMoE model [12] and apply it to learn multiple ranking objectives in Youtube video recommender systems. Moreover, PLE [17] achieves superior performance for news recommendation by assigning both shared expert sub-networks among tasks and dedicated expert sub-networks for each task. AITM [24] is the most similar method, which also augments the architecture of task towers. Nevertheless, as a concrete implementation, it is not validated to enhance the performance of various multi-task models. Moreover, it can only work for the tasks with sequential dependence relations. Admittedly, the aforementioned methods achieve impressive performance. However, as the task towers exert a direct effect on the prediction results, the standalone task towers tend not to be the most effective design for promoting positive knowledge transfer by exploiting the task relationships. First, for each task, the information selected by the relevant tasks is extremely valuable. Accordingly, we introduce the shared component of Cross Task Feature Mining(CTFM), which utilizes delicate attention mechanisms to extract relevant information from other tasks at the entry layer of the task tower. With the common attention mechanisms, the explicit task-specific information distilled by lower-level networks are mingled together and one task’s loss will undesirably affect the inputs of other task towers, which is the task awareness missing problem and can hinder the learning of lower-level networks. In contrast to the usual attention mechanisms, our design can ensure appropriate information separation. We argue that reserving explicit task-specific knowledge has a positive effect on performance, which is validated in the experimental section. Second, because the tasks for recommender systems are related, the training labels of multiple tasks should obey a joint distribution. The prediction results for these tasks should not densely fall into the low-density areas. Therefore, a dedicated network named Global Knowledge Distillation(GKD) is introduced for each task to distill valuable global knowledge from the results of the upper layer task towers. For each task, the distilled global information helps to ensure consistent predictions with other tasks. We summarize our main contributions below. • We propose the framework of Deep Mutual Learning across task towers(DML), which is compatible with various backbone multi-task models • The proposed novel sharing structure helps to enhance effective knowledge transfer across different tasks. • We conduct offline experiments and online A/B testing to evaluate and understand the effectiveness of our method. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com); (2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com); (3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com); (4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). Authors: Authors: (1) Yi Ren, Tencent, Beijing, China (yiren_bj@outlook.com); (2) Ying Du, Tencent, Beijing, China (yingdu@tencent.com); (3) Bin Wang, Tencent, Beijing, China (hillmwang@tencent.com); (4) Shenzheng Zhang, Tencent, Beijing, China (qjzcyzhang@tencent.com). Table of Links Abstract and 1 Introduction Abstract and 1 Introduction Methodology Experiments Conclusion and References Methodology Methodology Experiments Experiments Conclusion and References Conclusion and References ABSTRACT Recommender systems usually leverage multi-task learning methods to simultaneously optimize several objectives because of the multi-faceted user behavior data. The typical way of conducting multi-task learning(MTL) is to establish appropriate parameter sharing across multiple tasks at lower layers while reserving a separate task tower for each task at upper layers. With such design, the lower layers intend to explore the structure of task relationships and mine valuable information to be used by the task towers for accurate prediction. Since the task towers exert direct impact on the prediction results, we argue that the architecture of standalone task towers is suboptimal for promoting positive knowledge sharing. First, for each task, attending to the input information of other task towers is beneficial. For instance, the information useful for predicting the "like" task is also valuable for the "buy" task. Furthermore, because different tasks are inter-related, the training labels of multiple tasks should obey a joint distribution. It is undesirable for the prediction results for these tasks to fall into the low density areas. Accordingly, we propose the framework of Deep Mutual Learning across task towers(DML), which is compatible with various backbone multitask networks. At the entry layer of the task towers, the shared component of Cross Task Feature Mining(CTFM) is introduced to transfer input information across the task towers while still ensuring one task’s loss will not impact the inputs of other task towers. Moreover, for each task, dedicated network component called Global Knowledge Distillation(GKD) are utilized to distill valuable knowledge from the global results of the upper layer task towers to enhance the prediction consistency. Extensive offline experiments and online A/B tests are conducted to evaluate and verify the proposed approach’s effectiveness. 1. INTRODUCTION Recently, we have seen the widespread application of recommender systems, which involve different types of user feedback signals, such as clicking, rating, commenting, etc. Moreover, no single feedback signal can accurately reflect user satisfaction. For example, over-concentrating on clicking may aggravate the click-bait issue. Therefore, it is highly desirable to be able to effectively learn and estimate multiple types of user behaviors at the same time. And Multi-Task Learning is a promising technique to address this challenge. Given several related learning tasks, the goal of multi-task learning is to enhance the overall performance of different tasks by leveraging knowledge transfer among tasks. With the Multi-Task Learning(MTL) paradigm[3, 25], multiple tasks are learned simultaneously in a single model. Compared with single-task solutions, MTL costs much fewer machine resources and improves learning efficiency since we just need to train and deploy a single model. Moreover, MTL usually can warrant enhanced recommendation performance through appropriate parameter sharing. Most existing methods of Multi-Task Learning(MTL) appropriately share parameters across multiple tasks at lower layers while keeping separate task towers at upper layers. These methods can be roughly classified into four categories. The first category comprises the methods of hard parameter sharing . Among them, embedding sharing is the most intuitive structure to share information. For instance, the ESSM model [13] shares embedding parameters between the tasks of CTR (Click-Through Rate) and CVR (Conversion Rate) for improving the prediction performance of the sparse CVR task. In addition to the embedding parameters, the Shared-Bottom structure[3] is introduced to share the parameters of lower-layer MLPs among tasks. But these methods are severely plagued by the task conflicts and negative transfer issue. Second, for the methods of soft parameter sharing , each task owns separate parameters, which are regularized during training to minimize the differences between the shared parameters. L2-constrained[6] is a typical algorithm belonging to this category. Third, for the methods of customized routing , they learn customized routing weights for each task to combine and fuse information from lower-layer networks to counteract the negative transfer issue. Cross-stitch network[15] and sluice network[16] learn separate linear weights for each task to selectively merge representations from different lower-level branches. SNR[11] modularizes the shared low-level layers into parallel sub-networks and uses a transformation matrix multiplied by a scalar coding variable to learn their connections to upper-level layers to alleviate the task conflict and negative transfer issue. MSSM[5] learns differing combinations of feature fields for each expert and designs finer-grained sharing patterns among tasks through a set of coding variables that selectively choose which cells to route for a given task. But the learned routing parameters of these methods are static for all the samples, which can hardly warrant optimal performance. Finally, the methods of dynamic gating learn optimized weights for each task based on the input sample to effectively combine the outputs of lower-level networks and achieve success in industrial applications. The MMoE [12] model adapts the Mixture-of-Experts (MoE)[9] structure to multi-task learning by sharing the expert sub-networks across all tasks, while also maintaining separate gating network optimized for each task. And Zhao et al. [26] extend the MMoE model [12] and apply it to learn multiple ranking objectives in Youtube video recommender systems. Moreover, PLE [17] achieves superior performance for news recommendation by assigning both shared expert sub-networks among tasks and dedicated expert sub-networks for each task. hard parameter sharing hard parameter sharing soft parameter sharing soft parameter sharing customized routing dynamic gating dynamic gating AITM [24] is the most similar method, which also augments the architecture of task towers. Nevertheless, as a concrete implementation, it is not validated to enhance the performance of various multi-task models. Moreover, it can only work for the tasks with sequential dependence relations. Admittedly, the aforementioned methods achieve impressive performance. However, as the task towers exert a direct effect on the prediction results, the standalone task towers tend not to be the most effective design for promoting positive knowledge transfer by exploiting the task relationships. First, for each task, the information selected by the relevant tasks is extremely valuable. Accordingly, we introduce the shared component of Cross Task Feature Mining(CTFM), which utilizes delicate attention mechanisms to extract relevant information from other tasks at the entry layer of the task tower. With the common attention mechanisms, the explicit task-specific information distilled by lower-level networks are mingled together and one task’s loss will undesirably affect the inputs of other task towers, which is the task awareness missing problem and can hinder the learning of lower-level networks. In contrast to the usual attention mechanisms, our design can ensure appropriate information separation. We argue that reserving explicit task-specific knowledge has a positive effect on performance, which is validated in the experimental section. Second, because the tasks for recommender systems are related, the training labels of multiple tasks should obey a joint distribution. The prediction results for these tasks should not densely fall into the low-density areas. Therefore, a dedicated network named Global Knowledge Distillation(GKD) is introduced for each task to distill valuable global knowledge from the results of the upper layer task towers. For each task, the distilled global information helps to ensure consistent predictions with other tasks. We summarize our main contributions below. • We propose the framework of D eep M utual L earning across task towers( DML ), which is compatible with various backbone multi-task models D M L DML • The proposed novel sharing structure helps to enhance effective knowledge transfer across different tasks. • We conduct offline experiments and online A/B testing to evaluate and understand the effectiveness of our method. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

New Framework for Deep Mutual Learning (DML) to Improve Multi-task Recommender Systems

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk;
(2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk;
(3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction
Related work
Background
Methods
Experiments
"Conclusion, Limitations, and References"
Derivations
Algorithms
Experimental Datasets And Model Conclusion and Limitations We gave a new and far simpler method for computing posterior moments, marginals and samples in massively parallel importance sampling based on differentiating a slightly modified marginal likelihood estimator. The method has limitations, in that while it is considerably more effective than e.g. VI, HMC and global importance sampling, it is more complex. Additionally, at least a naive implementation may be quite costly in terms of memory consumption, limiting how the number of importance samples we can draw for each variable. That said, it should be possible to eliminate almost all of this overhead by careful optimizations to avoid allocating large intermediate tensors, following the strategy in KeOps (Charlier et al. 2021). References Aitchison, L. 2019. Tensor Monte Carlo: particle methods for the GPU era. Advances in Neural Information Processing Systems, 32. Albarghouthi, A.; D’Antoni, L.; Drews, S.; and Nori, A. V. 2017. Fairsquare: probabilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA): 1–30. Andrieu, C.; Doucet, A.; and Holenstein, R. 2010. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): 269–342. Bidyuk, B.; and Dechter, R. 2007. Cutset sampling for Bayesian networks. Journal of Artificial Intelligence Research, 28: 1–48. Bornschein, J.; and Bengio, Y. 2014. Reweighted wakesleep. arXiv preprint arXiv:1406.2751. Bradbury, J.; Frostig, R.; Hawkins, P.; Johnson, M. J.; Leary, C.; Maclaurin, D.; Necula, G.; Paszke, A.; VanderPlas, J.; Wanderman-Milne, S.; and Zhang, Q. 2018. JAX: composable transformations of Python+NumPy programs. Burda, Y.; Grosse, R.; and Salakhutdinov, R. 2015. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519. Charlier, B.; Feydy, J.; Glaunes, J. A.; Collin, F.-D.; and Du- ` rif, G. 2021. Kernel Operations on the GPU, with Autodiff,  without Memory Overflows. Journal of Machine Learning Research. Chatterjee, S.; and Diaconis, P. 2018. The sample size required in importance sampling. The Annals of Applied Probability, 28(2): 1099–1135. Claret, G.; Rajamani, S. K.; Nori, A. V.; Gordon, A. D.; and Borgstrom, J. 2013. Bayesian inference using data flow anal- ¨ ysis. In Proceedings of the 2013 9th joint meeting on foundations of software engineering, 92–102. Corenflos, A.; Chopin, N.; and Sarkk ¨ a, S. 2022. De- ¨ Sequentialized Monte Carlo: a parallel-in time particle smoother. arXiv preprint arXiv:2202.02264. Crucinio, F. R.; and Johansen, A. M. 2023. Properties of Marginal Sequential Monte Carlo Methods. ArXiv:2303.03498 [math, stat]. Dawid, A. P. 1992. Applications of a general propagation algorithm for probabilistic expert systems. Statistics and computing, 2(1): 25–36. Dechter, R.; Broka, F.; Kask, K.; and Ihler, A. 2018. Abstraction Sampling in Graphical Models. AAAI. Del Moral, P.; Doucet, A.; and Jasra, A. 2006. Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society Series B: Statistical Methodology, 68(3): 411–436. DOE. 2023. Bus Breakdown and Delays. url https://data.cityofnewyork.us/Transportation/BusBreakdown-and-Delays/ez4e-fazm Accessed on: 05.05.2023. Doucet, A.; Johansen, A. M.; et al. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, 12(656-704): 3. Geffner, T.; and Domke, J. 2022. Variational Inference with Locally Enhanced Bounds for Hierarchical Models. arXiv preprint arXiv:2203.04432. Gehr, T.; Misailovic, S.; and Vechev, M. 2016. PSI: Exact symbolic inference for probabilistic programs. In International Conference on Computer Aided Verification, 62–83. Springer. Geldenhuys, J.; Dwyer, M. B.; and Visser, W. 2012. Probabilistic symbolic execution. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, 166–176. Gogate, V.; and Dechter, R. 2012. Importance samplingbased estimation over and/or search spaces for graphical models. Artificial Intelligence, 184: 38–77. Goodman, N. D.; and Stuhlmuller, A. 2014. The design and implementation of probabilistic programming languages. Gordon, N. J.; Salmond, D. J.; and Smith, A. F. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE proceedings F (radar and signal processing), volume 140, 107–113. IET. Harper, F. M.; and Konstan, J. A. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4): 1–19. Heap, T.; and Laurence, A. 2023. Massively parallel reweighted wake-sleep. UAI. Hoffman, M. D.; Gelman, A.; et al. 2014. The No-UTurn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15(1): 1593–1623. Holtzen, S.; Van den Broeck, G.; and Millstein, T. 2020. Scaling exact inference for discrete probabilistic programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA): 1–31. Jordan, M. I.; Ghahramani, Z.; Jaakkola, T. S.; and Saul, L. K. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2): 183–233. Kingma, D. P.; Welling, M.; et al. 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4): 307–392. Kuntz, J.; Crucinio, F. R.; and Johansen, A. M. 2023. The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems. ArXiv:2110.15782 [math, stat]. Lai, J.; Domke, J.; and Sheldon, D. 2022. Variational Marginal Particle Filters. In International Conference on Artificial Intelligence and Statistics, 875–895. PMLR. Le, T. A.; Igl, M.; Rainforth, T.; Jin, T.; and Wood, F. 2017. Auto-encoding sequential monte carlo. arXiv preprint arXiv:1705.10306. Le, T. A.; Kosiorek, A. R.; Siddharth, N.; Teh, Y. W.; and Wood, F. 2020. Revisiting reweighted wake sleep for models with stochastic control flow. In Uncertainty in Artificial Intelligence, 1039–1049. PMLR. Lindsten, F.; Johansen, A. M.; Naesseth, C. A.; Kirkpatrick, B.; Schon, T. B.; Aston, J.; and Bouchard-Cotˆ e, A. 2017. ´ Divide-and-conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics, 26(2): 445–458. Maddison, C. J.; Lawson, J.; Tucker, G.; Heess, N.; Norouzi, M.; Mnih, A.; Doucet, A.; and Teh, Y. 2017. Filtering variational objectives. Advances in Neural Information Processing Systems, 30. Naesseth, C.; Linderman, S.; Ranganath, R.; and Blei, D. 2018. Variational sequential monte carlo. In International conference on artificial intelligence and statistics, 968–977. PMLR. Narayanan, P.; Carette, J.; Romano, W.; Shan, C.-c.; and Zinkov, R. 2016. Probabilistic inference by program transformation in Hakaru (system description). In International Symposium on Functional and Logic Programming, 62–79. Springer Obermeyer, F.; Bingham, E.; Jankowiak, M.; Pradhan, N.; Chiu, J.; Rush, A.; and Goodman, N. 2019. Tensor variable elimination for plated factor graphs. In International Conference on Machine Learning, 4871–4880. PMLR. Pfeffer, A. 2005. The design and implementation of IBAL: A generalpurpose probabilistic programming language. In Harvard Univesity. Citeseer. Salvatier, J.; Wiecki, T. V.; and Fonnesbeck, C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2: e55. Publisher: PeerJ Inc. Sankaranarayanan, S.; Chakarov, A.; and Gulwani, S. 2013. Static analysis for probabilistic programs: inferring whole program properties from finitely many paths. In Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation, 447–458. Wang, D.; Hoffmann, J.; and Reps, T. 2018. PMAF: an algebraic framework for static analysis of probabilistic programs. ACM SIGPLAN Notices, 53(4): 513–528. Weinberg, S. 1995. The quantum theory of fields, volume 2. Cambridge university press. Zavatone-Veth, J.; Canatar, A.; Ruben, B.; and Pehlevan, C. 2021. Asymptotics of representation learning in finite Bayesian neural networks. Advances in neural information processing systems, 34: 24765–24777. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sam Bowyer, Equal contribution, Department of Mathematics and sam.bowyer@bristol.ac.uk; (2)  Thomas Heap, Equal contribution, Department of Computer Science University of Bristol and thomas.heap@bristol.ac.uk; (3) Laurence Aitchison, Department of Computer Science University of Bristol and laurence.aitchison@bristol.ac.uk. Table of Links Abstract & Introduction Related work Background Methods Experiments "Conclusion, Limitations, and References" Derivations Algorithms Experimental Datasets And Model Abstract & Introduction Abstract & Introduction Related work Related work Background Background Methods Methods Experiments Experiments "Conclusion, Limitations, and References" "Conclusion, Limitations, and References" Derivations Derivations Algorithms Algorithms Experimental Datasets And Model Experimental Datasets And Model Conclusion and Limitations We gave a new and far simpler method for computing posterior moments, marginals and samples in massively parallel importance sampling based on differentiating a slightly modified marginal likelihood estimator. The method has limitations, in that while it is considerably more effective than e.g. VI, HMC and global importance sampling, it is more complex. Additionally, at least a naive implementation may be quite costly in terms of memory consumption, limiting how the number of importance samples we can draw for each variable. That said, it should be possible to eliminate almost all of this overhead by careful optimizations to avoid allocating large intermediate tensors, following the strategy in KeOps (Charlier et al. 2021). References Aitchison, L. 2019. Tensor Monte Carlo: particle methods for the GPU era. Advances in Neural Information Processing Systems, 32. Albarghouthi, A.; D’Antoni, L.; Drews, S.; and Nori, A. V. 2017. Fairsquare: probabilistic verification of program fairness. Proceedings of the ACM on Programming Languages, 1(OOPSLA): 1–30. Andrieu, C.; Doucet, A.; and Holenstein, R. 2010. Particle markov chain monte carlo methods. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(3): 269–342. Bidyuk, B.; and Dechter, R. 2007. Cutset sampling for Bayesian networks. Journal of Artificial Intelligence Research, 28: 1–48. Bornschein, J.; and Bengio, Y. 2014. Reweighted wakesleep. arXiv preprint arXiv:1406.2751. Bradbury, J.; Frostig, R.; Hawkins, P.; Johnson, M. J.; Leary, C.; Maclaurin, D.; Necula, G.; Paszke, A.; VanderPlas, J.; Wanderman-Milne, S.; and Zhang, Q. 2018. JAX: composable transformations of Python+NumPy programs. Burda, Y.; Grosse, R.; and Salakhutdinov, R. 2015. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519. Charlier, B.; Feydy, J.; Glaunes, J. A.; Collin, F.-D.; and Du- ` rif, G. 2021. Kernel Operations on the GPU, with Autodiff,  without Memory Overflows. Journal of Machine Learning Research. Chatterjee, S.; and Diaconis, P. 2018. The sample size required in importance sampling. The Annals of Applied Probability, 28(2): 1099–1135. Claret, G.; Rajamani, S. K.; Nori, A. V.; Gordon, A. D.; and Borgstrom, J. 2013. Bayesian inference using data flow anal- ¨ ysis. In Proceedings of the 2013 9th joint meeting on foundations of software engineering, 92–102. Corenflos, A.; Chopin, N.; and Sarkk ¨ a, S. 2022. De- ¨ Sequentialized Monte Carlo: a parallel-in time particle smoother. arXiv preprint arXiv:2202.02264. Crucinio, F. R.; and Johansen, A. M. 2023. Properties of Marginal Sequential Monte Carlo Methods. ArXiv:2303.03498 [math, stat]. Dawid, A. P. 1992. Applications of a general propagation algorithm for probabilistic expert systems. Statistics and computing, 2(1): 25–36. Dechter, R.; Broka, F.; Kask, K.; and Ihler, A. 2018. Abstraction Sampling in Graphical Models. AAAI. Del Moral, P.; Doucet, A.; and Jasra, A. 2006. Sequential Monte Carlo Samplers. Journal of the Royal Statistical Society Series B: Statistical Methodology, 68(3): 411–436. DOE. 2023. Bus Breakdown and Delays. url https://data.cityofnewyork.us/Transportation/BusBreakdown-and-Delays/ez4e-fazm Accessed on: 05.05.2023. Doucet, A.; Johansen, A. M.; et al. 2009. A tutorial on particle filtering and smoothing: Fifteen years later. Handbook of nonlinear filtering, 12(656-704): 3. Geffner, T.; and Domke, J. 2022. Variational Inference with Locally Enhanced Bounds for Hierarchical Models. arXiv preprint arXiv:2203.04432. Gehr, T.; Misailovic, S.; and Vechev, M. 2016. PSI: Exact symbolic inference for probabilistic programs. In International Conference on Computer Aided Verification, 62–83. Springer. Geldenhuys, J.; Dwyer, M. B.; and Visser, W. 2012. Probabilistic symbolic execution. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, 166–176. Gogate, V.; and Dechter, R. 2012. Importance samplingbased estimation over and/or search spaces for graphical models. Artificial Intelligence, 184: 38–77. Goodman, N. D.; and Stuhlmuller, A. 2014. The design and implementation of probabilistic programming languages. Gordon, N. J.; Salmond, D. J.; and Smith, A. F. 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation. In IEE proceedings F (radar and signal processing), volume 140, 107–113. IET. Harper, F. M.; and Konstan, J. A. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis), 5(4): 1–19. Heap, T.; and Laurence, A. 2023. Massively parallel reweighted wake-sleep. UAI. Hoffman, M. D.; Gelman, A.; et al. 2014. The No-UTurn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo. J. Mach. Learn. Res., 15(1): 1593–1623. Holtzen, S.; Van den Broeck, G.; and Millstein, T. 2020. Scaling exact inference for discrete probabilistic programs. Proceedings of the ACM on Programming Languages, 4(OOPSLA): 1–31. Jordan, M. I.; Ghahramani, Z.; Jaakkola, T. S.; and Saul, L. K. 1999. An introduction to variational methods for graphical models. Machine learning, 37(2): 183–233. Kingma, D. P.; Welling, M.; et al. 2019. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4): 307–392. Kuntz, J.; Crucinio, F. R.; and Johansen, A. M. 2023. The divide-and-conquer sequential Monte Carlo algorithm: theoretical properties and limit theorems. ArXiv:2110.15782 [math, stat]. Lai, J.; Domke, J.; and Sheldon, D. 2022. Variational Marginal Particle Filters. In International Conference on Artificial Intelligence and Statistics, 875–895. PMLR. Le, T. A.; Igl, M.; Rainforth, T.; Jin, T.; and Wood, F. 2017. Auto-encoding sequential monte carlo. arXiv preprint arXiv:1705.10306. Le, T. A.; Kosiorek, A. R.; Siddharth, N.; Teh, Y. W.; and Wood, F. 2020. Revisiting reweighted wake sleep for models with stochastic control flow. In Uncertainty in Artificial Intelligence, 1039–1049. PMLR. Lindsten, F.; Johansen, A. M.; Naesseth, C. A.; Kirkpatrick, B.; Schon, T. B.; Aston, J.; and Bouchard-Cotˆ e, A. 2017. ´ Divide-and-conquer with sequential Monte Carlo. Journal of Computational and Graphical Statistics, 26(2): 445–458. Maddison, C. J.; Lawson, J.; Tucker, G.; Heess, N.; Norouzi, M.; Mnih, A.; Doucet, A.; and Teh, Y. 2017. Filtering variational objectives. Advances in Neural Information Processing Systems, 30. Naesseth, C.; Linderman, S.; Ranganath, R.; and Blei, D. 2018. Variational sequential monte carlo. In International conference on artificial intelligence and statistics, 968–977. PMLR. Narayanan, P.; Carette, J.; Romano, W.; Shan, C.-c.; and Zinkov, R. 2016. Probabilistic inference by program transformation in Hakaru (system description). In International Symposium on Functional and Logic Programming, 62–79. Springer Obermeyer, F.; Bingham, E.; Jankowiak, M.; Pradhan, N.; Chiu, J.; Rush, A.; and Goodman, N. 2019. Tensor variable elimination for plated factor graphs. In International Conference on Machine Learning, 4871–4880. PMLR. Pfeffer, A. 2005. The design and implementation of IBAL: A generalpurpose probabilistic programming language. In Harvard Univesity. Citeseer. Salvatier, J.; Wiecki, T. V.; and Fonnesbeck, C. 2016. Probabilistic programming in Python using PyMC3. PeerJ Computer Science, 2: e55. Publisher: PeerJ Inc. Sankaranarayanan, S.; Chakarov, A.; and Gulwani, S. 2013. Static analysis for probabilistic programs: inferring whole program properties from finitely many paths. In Proceedings of the 34th ACM SIGPLAN conference on Programming language design and implementation, 447–458. Wang, D.; Hoffmann, J.; and Reps, T. 2018. PMAF: an algebraic framework for static analysis of probabilistic programs. ACM SIGPLAN Notices, 53(4): 513–528. Weinberg, S. 1995. The quantum theory of fields, volume 2. Cambridge university press. Zavatone-Veth, J.; Canatar, A.; Ruben, B.; and Pehlevan, C. 2021. Asymptotics of representation learning in finite Bayesian neural networks. Advances in neural information processing systems, 34: 24765–24777.

Using Autodiff to Estimate Posterior Moments: Conclusion, Limitations, and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Qin Wang, CSIRO Data61, Australia; (2) Shiping Chen, CSIRO Data61, Australia. Table of Links Abstract & Introduction Ethereum Accounts Account Abstraction Security Analysis Further Discussions Conclusion & References VI. CONCLUSION In this paper, we explore the notion of account abstraction (featured by EIP-4337), which is formally involved the sixth stage of Ethereum’s roadmap. We study its operating mechanism, key features, and surrounding developments. We also examine its security by assessing a set of related criteria. Our results reveal the scope and extent of the security improvements introduced by the adoption of account abstraction. To our knowledge, this work provides the first formal AA study. REFERENCES [1] G. Wood et al., “Ethereum: A secure decentralised generalised transaction ledger,” Ethereum project yellow paper, vol. 151, no. 2014, pp. 1–32, 2014. [2] @jmcook1186, “Ethereum accounts,” Retrived https://ethereum.org/en/developers/docs/ accounts/ , 2023. [3] “Ethereum: ERC-4337,” https://www.erc4337.io/ , 2023. [4] Ethereum, “Ethereum roadmap: Account abstraction,” https://ethereum.org/en/ roadmap/account-abstraction/ , 2023. [5] T. T., “Ethereum EVM illustrated,” https://takenobu-hs.github.io/ downloads/ethereum evm illustrated.pdf , 2023. [6] A. K. Singh, I. U. Hassan, G. Kaur, S. Kumar et al., “Account abstraction via singleton entrypoint contract and verifying paymaster,” International Conference on Edge Computing and Applications (ICECAA), pp. 1598– 1605, 2023. [7] C. Colin, “Biance research: A primer on account abstraction,” https:// research.binance.com/static/ pdf/ a-primer-on-account-abstraction.pdf , 2023. [8] A. Emmanuel, “Account abstraction: Past, present, future,” https://metamask.io/news/latest/ account-abstraction-past-present-f uture/ , 2023. [9] Alchemy, “How ERC-4337 supports account abstraction,” https://www.alchemy.com/learn/ account-abstraction, 2023. [10] Moderate, “Coinmarketcap: Account abstraction,” https://coinmarketcap.com/ alexandria/glossary/account-abstraction, 2023. [11] aleksandrmalyshev, “What is account abstraction and why is everyone talking about it?” https://hackernoon.com/what-is-account-abstraction-and-why-is-everyone-talking-about-it, 2023. [12] Q. Wang, R. Li, Q. Wang, S. Chen, M. Ryan, and T. Hardjono, “Exploring Web3 from the view of blockchain,” arXiv preprint arXiv:2206.08821, 2022. [13] S. Park, J. H. Lee, S. Lee, J. H. Chun, H. Cho, M. Kim, H. K. Cho, and S.-M. Moon, “Beyond the blockchain address: Zero-knowledge address abstraction,” Cryptology ePrint Archive, 2023. [14] Q. Wang, R. Li, Q. Wang, and S. Chen, “Non-fungible token (NFT): Overview, evaluation, opportunities and challenges,” arXiv preprint arXiv:2105.07447, 2021. [15] “Sapienz wallet,” https://www.sapienz.xyz/ , 2023. [16] B. Andrew, G. Catherine, R. Srini, M. Mohsen, and K. Ranjit, “Visa: Auto payments for self-custodial wallets,” https://usa.visa.com/solutions/crypto/ auto-payments-f or-self-custodial-wallets.html, 2023. [17] “Safe wallet,” https:// safe.global/wallet, 2023. [18] “Argent wallet,” https://www.argent.xyz/ , 2023. [19] “Bravvos wallet,” https://braavos.app/ , 2023. [20] H. Chen, M. Pendleton, L. Njilla, and S. Xu, “A survey on Ethereum systems security: Vulnerabilities, attacks, and defenses,” ACM Computing Surveys (CSUR), vol. 53, no. 3, pp. 1–43, 2020. [21] P. Tsankov, A. Dan, D. Drachsler-Cohen, A. Gervais, F. Buenzli, and M. Vechev, “Securify: Practical security analysis of smart contracts,” in Proceedings of the ACM SIGSAC Conference on Computer and Communications Security (CCS), 2018, pp. 67–82. [22] L. Su, X. Shen, X. Du, X. Liao, X. Wang, L. Xing, and B. Liu, “Evil under the sun: Understanding and discovering attacks on ethereum decentralized applications,” in USENIX Security Symposium (USENIX Sec), 2021, pp. 1307–1324. [23] P. Tolmach, Y. Li, S.-W. Lin, Y. Liu, and Z. Li, “A survey of smart contract formal specification and verification,” ACM Computing Surveys (CSUR), vol. 54, no. 7, pp. 1–38, 2021. [24] W. Zou, D. Lo, P. S. Kochhar, X.-B. D. Le, X. Xia, Y. Feng, Z. Chen, and B. Xu, “Smart contract development: Challenges and opportunities,” IEEE Transactions on Software Engineering (TSE), vol. 47, no. 10, pp. 2084–2106, 2019. [25] J. Krupp and C. Rossow, “teEther: Gnawing at Ethereum to automatically exploit smart contracts,” in USENIX Security Symposium (USENIX Sec), 2018, pp. 1317–1333. [26] L. Brent, N. Grech, S. Lagouvardos, B. Scholz, and Y. Smaragdakis, “Ethainter: a smart contract security analyzer for composite vulnerabilities,” in Proceedings of the ACM SIGPLAN Conference on Programming [27] Z. Wan, X. Xia, D. Lo, J. Chen, X. Luo, and X. Yang, “Smart contract security: A practitioners’ perspective,” in IEEE/ACM International Conference on Software Engineering (ICSE). IEEE, 2021, pp. 1410–1422. [28] X. Zhang et al., “Time-manipulation attack: Breaking fairness against proof of authority Aura,” in Proceedings of the ACM Web Conference (WWW), 2023, pp. 2076–2086. [29] Q. Wang, B. Qin, J. Hu, and F. Xiao, “Preserving transaction privacy in Bitcoin,” Future Generation Computer Systems (FGCS), vol. 107, pp. 793–804, 2020. [30] A. Kosba, A. Miller, E. Shi, Z. Wen, and C. Papamanthou, “Hawk: The blockchain model of cryptography and privacy-preserving smart contracts,” in IEEE Symposium on Security and Privacy (SP). IEEE, 2016, pp. 839–858. [31] R. Li et al., “SoK: TEE-assisted confidential smart contract,” Proceedings on Privacy Enhancing Technologies, vol. 3, pp. 711–731, 2022. [32] G. Yu, X. Wang et al., “Towards web3 applications: Easing the access and transition,” arXiv preprint arXiv:2210.05903, 2022.

Account Abstraction, Analysed: Conclusion & References

240 Stories To Learn About Startups Of The Year

Hey, fellow tech enthusiasts. Are you contemplating and envisioning your autonomous vehicle on the road being able to automatically adjust and adapt as per the traffic scenarios and road conditions ahead, without you having to interfere or be concerned? Well, that has always been the intention of innovators and AI technologists. However, there have been concerning faults and/or rather room for improvement pertaining to self-driving cars. Well, to make self-driving cars all the more safe, sound, and smart, we have the most intelligent technology – Liquid Neural Networks. In this article, we will delve deeper to know how the challenges of safety and longevity identified in . Also, how LNNs make use of already present training datasets self-driving cars to date can be controlled using liquid neural networks What Are Liquid Neural Networks? In a nutshell, NNs are machine learning algorithms that mimic the structure and capability of the human brain. They are used to recognize patterns by analyzing training data. In addition to recognizing faces, understanding natural languages, and predicting the future, neural networks are capable of performing complex tasks without the intervention of humans through their network of interconnected artificial neurons. Liquid Neural Networks VS Traditional Networks Traditional neural networks are regarded as one of the most powerful Artificial Intelligence tools. But it comes along with a few limitations, majorly: Training data, which must be annotated and labeled in a substantial amount. 1. Also, they have been found to be inefficient at utilizing the enormous amount of input data to tackle real-time scenarios, due to the non-sequentiality of their processing 2. In order to overcome these two major challenges, Ramin Hasani and Mathias Lechner were the two researchers in MIT's computer science and AI lab who invented Liquid Neural Networks. They found their inspiration in a 1 mm long work that has an impressive structured nervous system and can perform tasks as complex as searching for food, going off to sleep, and most importantly, learning briskly by observing the surrounding environment. Similarly, LNNs are an advanced type of neural network that learns on the go and takes necessary action on the spot. While most traditional networks perform by the data they are fueled with during the training period, LNNs have proved to be all the more adaptable. LNNs are able to read, learn, and respond on the go, on the spot by ‘OBSERVING’ the impromptu input. Liquid Neural Network Dynamic Architechture Self-Expressive Interpretable, ability to take instant action Ability to learn continuously and on the fly Traditional Neural Network Static Architecture Express only what is taught Non-interpretable, take action as per training input only Limited learning - only during the training period How do Liquid Neural Networks Facilitate Autonomous Vehicles Production? Liquid Neural Networks are undoubtedly an elegant, fast, and reliable alternative to traditional neural networks. It’s like a creature living in real conditions - understands what’s happening at present, can predict the near future, and act accordingly. Liquid Neural Networks for Autonomous Vehicles Uses Cases Imagine getting into a self-driven vehicle without having to worry about the anonymity of the input training data. You know the car will adapt and adjust as per the situation on the road. You can gleefully hop onto your driver-less car and enjoy your ride to your destination: • No worrying about swimming cautiously through the sea of unusual traffic. • Moving through uneven roads and reaching unknown destinations will become easier •Problems like crossing speed limits and getting into a ‘no-Uturn’ area unintentionally, will decline. •Roads, drivers, and driving styles will almost be the same for one and all. •Difference between the rich and poor will decrease, as everyone will automatically follow the same rules. •The camaraderie between insurance companies and can owners will improve as conditions and situations will become transparent. Wrapping It Up Claiming Liquid Neural Networks to be a boon for the industry will not be an overstatement. It will not only increase the production and sales of autonomous vehicles but also make the life of vehicle owners and drivers easy. self-driving vehicle Therefore, self-driving vehicles will not only become more efficient but also gain immense popularity with time.

Liquid Neural Networks: Adept Invention for Adaptable Self-Driving Cars

Redes neuronales líquidas: invención experta para autos autónomos adaptables

Réseaux de neurones liquides : une invention experte pour des voitures autonomes adaptables

लिक्विड न्यूरल नेटवर्क: अनुकूलनीय सेल्फ-ड्राइविंग कारों के लिए कुशल आविष्कार

リキッド ニューラル ネットワーク: 適応可能な自動運転車のための優れた発明

Redes neurais líquidas: invenção especializada para carros autônomos adaptáveis

Mạng thần kinh lỏng: Phát minh lão luyện cho ô tô tự lái có thể thích ứng

液体神经网络：自适应自动驾驶汽车的巧妙发明

* * Achievement unlocked: With more than ‘4 billion’ in total votes -- almost half the population of earth voted for startups! JUST KIDDING! Some companies had a to accumulating their votes. unique approach In 2021, decided to host to highlight the backbone of the future - tech startups! for the first time in forever (make sure you sing it in Anna’s voice), HackerNoon.com Startups of The Year More than 44,000 startups were nominated all across the globe, spanning over 4,553 cities. That’s.Just.Spiffy! To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review.) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement Series We’d like to take a moment here and thank every single person who cast a vote and every single company, both nominated and not nominated yet this year. Furthermore, we thank you for existing and opening more opportunities for the wonders of the future to come. Even if you don’t win anything today, you’ve won something much more valuable - our hearts <3 Okay, sorry for the long introduction. Below are all the Startups Winners: Startups of the Year (SOTY) in Africa Johannesburg, South Africa With more than 900 votes cast, #SOTY calls.... MiBRAND Thank you for connecting businesses and consumers, great job! Lagos, Nigeria With 836 votes in the bag, let’s give it up for... Goodtalent Thanks for helping tech companies build great engineering teams. Cape Town, South Africa Want to learn more about finance and use that knowledge to make better financial decisions? Look no further. Introducing winner of Startups of The Year, Cape Town: Revix With 527 votes cast, it seems like a lot of people have more money in their pockets thanks to . Revix Mahé, Seychelles With around 515 votes cast, #SOTY summons... DAOLaunch Thank you for bridging the gap between investors and startups. Lusaka And the winner for #SOTY in Lusaka is... ComGrow Thanks to them, people here are getting closer to the reality of the digitization of village banking. Nairobi, Kenya With 252 votes cast, UTU came out top as the Startup of The Year. Thanks for building the trust infrastructure of the internet to help businesses and consumers engage and transact in a more reliable way. Yaoundé Accounting for more than 73% of total votes, CamPay won this award fair and square. Thank you for making sending and receiving payments easier between online merchants. Abuja, Nigeria A proud winner in the prestigious capital of Nigeria, Elesaro deserves all the plaudits it gets. is a suite crowdfunding protocol built on BSC, Reward, Equity, Lending & NFT Marketplace. Congratulations! Elesaro Dar Es Salaam, Tanzania Jamii Africa is the name that will always be engraved on the #SOTY trophy. As a mobile micro-health insurance, Jamii Africa has done a great job for the health sector in Tanzania. Kigali, Rwanda Another #SOTY medal in the bag for none other than …wait for it… BAG Innovation BAG Innovation applies Gamification to offer real-time access to experience-based learning for University students in East Africa. Everybody loves playing games, but using games to learn in school is on another level. Gauteng, South Africa And the award goes to... Planet42 Thanks for democratising mobility by enabling people ignored by banks to get access to a personal vehicle through a car subscription service. Once again, congratulations! Port Harcourt, Nigeria Cryptocurrency is the present and the future, as we’ve all come to know. FLITBASE makes these digital assets more accessible to everyone. #SOTY is proud to add you to our Hall of Fame. Cairo, Egypt With 18 votes won out of the 91 cast, it seems there was fierce competition. Congratulations to... SubsBase for winning Startups of The Year. SubsBase allows businesses to manage their subscribers, automate recurring billings, set pricing plans, and monitor subscription KPIs. Tunis, Tunisia Come one, come all! Witness the greatness of yet another wonderful startup in the #SOTY. Thank you... VYND for providing a fully integrated digital platform to help venue owners better serve their customers and grow their businesses. Stellenbosch, South Africa BankTech connects clients to a payment network where it provides a convenient interface to meet various payments needs. You’ve won #SOTY proudly! Pretoria #SOTY acknowledges... Grays Pharmaceuticals Thank you for all you do. Lekki, Nigeria Middletrust provides reliable escrow services in Africa. Congratulations on winning a #SOTY medal. Addis Ababa Thank you... Hello Solar for consistently accelerating sustainable energy solutions, especially for remote communities. With half of the votes cast, you beat the odds. Accra, Ghana UT Bank Ghana provides various commercial banking services to individuals and companies in Ghana. We cannot emphasize enough the importance of financial services to humankind. #SOTY is proud of all you do. Gîza, Egypt Big congratulations to... HOVO for winning this award. Your on-demand mobile app for ordering trucks is truly appreciated! Goma, Democratic Republic of Congo Congratulations for coming out on top of #SOTY, SillonBlue Technologies As an ICT Consulting and SaaS company, SillonBlue Technologies focuses on software and mobile app development, and data collection. Yaba, Nigeria With more than half of the votes cast, this nominee conquered undisputedly. Let’s give it up for... Kuda a no-fee full-service digital bank making banking affordable for all Africans on the planet. Morocco City Almes provides infrastructure and logistics services. Congratulations on earning a #SOTY medal. Dakar ShopMeAway provides families and businesses with new ways to shop around the world with reduced shipping costs. You deserve the plaudits you’ve received! Casablanca Sheaply provides crowd shipping and crowd shopping solutions. This reduces cost and improves efficiency on shipping and shopping. Congratulations on winning a #SOTY medal. Nguru, Nigeria Accounting for 88% of the votes, TradeBuza wins #SOTY almost flawlessly. TradeBuza is a tech-enabled commodities sourcing platform for commodities aggregators and out-grower schemes. Ikeja, Nigeria PropertyPro Nigeria is now pronounced Startup of the Year in Ikeja. Let’s admit, who doesn’t appreciate a platform built to search for real estate properties? Ibadan, Nigeria No Bank, No Problem! (That’s probably not their motto, by the way) Shield Finders is a fintech and financial service provider to the unbanked. Congratulation on winning most of the votes! 6 October City, Egypt Wuilt is a website builder where you can create your own Arabic website in under 5 minutes. Thank you for making this technology accessible to people. One #SOTY medal for you! Algiers The only nominee who outvoted all others, please give it up for... Yassir Thank you for providing convenient transport services using your app. Enugu In an area that usually suffers from inconsistent electricity, Greenage comes to the rescue with their green cape and spandex suit. Greenage Technologies is a Nigerian startup solving electricity problems in Nigeria using solar energy technology. Congrats, Greenage. Abeokuta, Nigeria A government organization creating opportunities for entrepreneurs and the unemployed - no one deserves the title Startup of The Year better than Ogun Innovate Congratulations! Ariana And the award goes to... E-Stores Factory a web platform that helps rent and manage e-shops for enterprises seeking to sell online. Thank you for making the (not a typo) more friendly! e-cosystem Benin City Health really is wealth. MOBicure provides mobile technology solutions for Africa's biggest health problems. Thank you for all you do. From #SOTY with love <3 Centurion GameZBoost offers white label gaming platforms, including online desktop solutions, fully responsive mobile and social platforms. Thank you for making the world a more fun place! Gaborone Augmenta Cyber Security provides services in cyber security, safe data storage, SCADA security, SAP Security, etc. Congratulations on winning 10/10 of the votes. You rock! Garki There can never be enough medical platforms in the world. Congratulations to... Wella Health an operator of a healthcare technology company intended to provide treatments and medicines for malaria. Thank you for all you do! Lilongwe Yellow is a digital platform enabling informal merchants in Africa. #SOTY welcomes you to its hall of fame. Make yourself at home ;) Luanda Access to finance is important for any company or sector to succeed. And no other company deserves the title Startup of The Year more than Deya the first Angolan and PALOP crowdfunding platform that aims to increase access to financing for social impact causes and entrepreneurs. . 10/10 will recommend Mombasa Life can be hard, and so a little humor helps ease these hardships. Congratulations to this political satire puppet show, The XYZ Show for winning #SOTY. Thanks for putting smiles on our faces. Port Louis Town Bet you’ve never heard of a Smartcup. Thanks to Intellicup that exists now, and therefore earns this startup a #SOTY badge.  Intellicup is a unique high-speed beverage dispensing solution with integrated smart technology. While this cup won’t be able to wake you up in the morning, you have to agree that it’s still pretty impressive. Rabat Atlan Space is a startup building AI to autonomously pilot any fixed-wing drone, to monitor and take measures over very large areas. You deserve the #SOTY medal. Réunion Imagine having the power to do ultrasound scans on your handheld device. OSCULT by Oscadi is a handheld ultrasound imaging device interfaced with the Apple iPad. Congratulations on making it into the #SOTY! Somerset West Winning the award fair and square is Sticitt a cashless payment platform that is also aimed at teaching learners at school the basics of finance. The future of the world would be in good hands when our children know how to adequately manage finances. Thank you for saving the world in advance. Thika Flowers make the world a beautiful place to live in. Wilmar Flowers exports summer flowers grown by small and medium-sized farmers from Kenya. Kudos for making the world beautiful, #SOTY is honored to have you in our Hall of Fame. Kampala, Uganda Yo-Waste Limited is an African startup that provides waste, recycling and “smart city” solutions to businesses and governments. Your #SOTY gold medal shines as much as you do <3 Harare, Zimbabwe The world is becoming more and more digital every day. Thanks to companies like Golix that provide services of digital currencies for people in Africa, the digitization of the world is becoming more robust. Freetown Easy Solar is a distribution company making energy, durable goods and financial services affordable and accessible through pay-as-you-go tech. Thumbs up! Kicukiro FOODLINE specializes in non-genetically-modified grocery farming and production lines. Winner Winner Chicken Dinner! #SOTY Goma, Democratic Republic of Congo Startups Nuru is a renewable and environmental company that specializes in the fields of storage micro-grids and solar energy. After racking up all the votes, #SOTY honors you. Abidjan, Côte d'Ivoire Digitech provides a fully integrated and cloud-based InsurTech platform to incumbent insurance companies. You are awesome! Durban Security can never really be enough. We’d like to honor Blue Security the title Startup of The Year, for specializing in the installation and monitoring of intruder detection systems, and in providing armed response services. Ikoyi And the Startup of The year goes to... Winich Farms an Agritech Startup leveraging tech to connect rural farmers to Finance & Market, thus, increasing productivity and cutting post-harvest loss. You rock! Lomé The last recipient of #SOTY in Africa calls for... Gozem a transport technology platform for Francophone West and Central Africa. Thank you for providing a platform for transportation. We are honored to have you! *Winners missing from this list will be added accordingly. More Information If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written and edited by Kien Dao and Joshua Omale. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

What are the Top Startups in Africa?

Thank you to the problem solvers who campaigned extra hard in the first-ever competition! HackerNoon Startups of the Year focuses on recognizing the passion and stamina of those willing to find and create effective tech solutions for their cities. HackerNoon’s Startups of the Year To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Awards, where cheaters were caught, are under review.) startups.hackernoon.com From all of us at , thank YOU for your excellent campaign perseverance🥺! We look forward to seeing you again in the 2022 campaign (stay tuned 😉)! HackerNoon Without further adieu, let us kick off by sharing the cities with top votes per region for 2021 🎉 . our series of Best Startups of the Year Stay tuned for the rest of the winners per region to be over the course of this week (Feb 14, 2022). announced North and Central America America is the concrete jungle where dreams are made of, the world’s capital of fashion, showbiz, business, major industry, etc., and surely, of Startups! Representing , we have: North and Central America (14,563 votes): Winner: Boulder Bury.com (10,521 votes) - Winner: New York City Hum Capital (7,987 votes) - Winner: San Francisco Zentist (7,713 votes) - Winner: Redwood City TigerGraph (469 votes) - Winner: Los Altos Stobox To see more awards in this region please click here. Europe Charming, sophisticated, cosmopolitan. There are many beautiful words used to describe this region. With its eccentric vibe, is also a place where new Startups thrive. Europe Representing Europe we have: (10, 010 votes) - Winner: Belfast Bob & Berts (5, 066 votes) - Winner: Ghent WeGroup (4, 712 votes) - Winner: Porto TAIKAI (3, 779 votes) - Winner: City of London Crypto Quantique To see more awards in this region please click here. Oceania The heat, the beaches, roaming kangaroos, or tarantulas? No. Startups! Representing the region of are: Oceania (1, 011 votes) - Winner: Sydney shotstack (430 votes) - Winner: Melbourne Doohly (118 votes) - Winner: Gold Coast Easil (76 votes) - Winner: Tauranga LawVu (40 votes) - Winner: Western Junction Today We Learned To see more awards in this region please click here. Asia Diverse in both ethnicity and culture, known for exotic sceneries as well as friendly people. Many Asian countries are on the verge of mass globalization and modernization. These fast-developing economies give way for many potential Startups. We present, for : Asia (12, 686 votes) - Winner: Singapore Cake DeFi (3,355 votes) - Winners: and (It’s a tie, we’re working on it!) Tel Aviv Applitools hyro (2, 402 votes) - Winner: Beirut Hovi Digital Lab (1.006 votes) - Winner: Hyderabad Quixy (756 votes) - Winner: Hanoi DeFi For You To see more city awards in this region please click here. Africa The second biggest continent in the world, home to many big animals, the longest river in the world AND the largest desert in the world. is known for many big things and sure enough, big Startups. Africa Representing Africa, we have: (884 votes) - Winner: Johannesburg MiBRAND (804 votes) - Winner: Lagos Goodtalent (526 votes) - Winner: Cape Town Revix (420 votes) - Winner: Lusaka ComGrow (224 votes) - Winner: Nairobi UTU To see more city awards in this region please click here. South America “South America is the land of the extreme.” - National Geographic Extremely beautiful people? Yes. Extremely worth-noticeable Startups? Hell yeah! We proudly present, Startups in : South America (217 votes ) - Winner: São Carlos Predify (193 votes) - Winner: Belo Horizonte CM Technologia (106 votes) - Winner: Porto Alegre Privacy Tools (82 votes) - Winner: Quito Agrodemanda (55 votes) -Winner: Rio De Janeiro Bybrand To see more city awards in this region please click here. Once again, and to all of the top Startups of each region this year! HUGE CONGRATULATIONS BIG PROPS The whole team at would like to thank you for your excellent campaign perseverance and we look forward to seeing you in the 2022 campaign. HackerNoon As part of honoring your participation, we will continue to host your company in this directory until further notice ( for legal reasons we hesitate to say ‘forever’..but pretty much forever).  As traffic is directed to the site, it will act as a promotional platform for your company. Have a great Valentine’s Day, HackerNoon loves y’all <3 More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network This post was written by Tuan Anh Vu. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

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