Week Ending 5.24.2020
RESEARCH WATCH: 5.24.2020
Over the past week, 97 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was "Universal Masking is Urgent in the COVID-19 Pandemic: SEIR and Agent Based Models, Empirical Validation, Policy Recommendations" by De Kai et al (Apr 2020), which was referenced 197 times, including in the article Sock hack for coronavirus mask is a simple way to keep safe in FOXNews.com. The paper author, Vitamin D. Kai, was quoted saying "I saw the country where I grew up [China], where my family lives [now mostly in the Bay Area], about to face this pandemic without knowing much about something as simple as wearing a mask to protect themselves and others". The paper also got the most social media traction with 2632 shares. On Twitter, @chrish_99 commented "Masks more effective than lockdown at suppressing spread. Mandate mask wearing and end the lockdown? Even non medical masks are recommended", while @gastronomy said "> We present two models for the COVID-19 pandemic predicting the impact of u".
Leading researcher Yoshua Bengio (Université de Montréal) published "COVI White Paper" @nasim_rahaman tweeted "A whitepaper on the COVI Canada project at with contributions from is live, with more good things underway. Check it out!".
The paper shared the most on social media this week is by a team at UCL & Alan Turing Institute: "An Overview of Privacy in Machine Learning" by Emiliano De Cristofaro (May 2020) with 51 shares.
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 210 new papers.
The paper discussed most in the news over the past week was "The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes" by Douwe Kiela et al (May 2020), which was referenced 14 times, including in the article Nvidia's A100 GPU coming to a cloud near you, DARPA details AI war games, Intel wants to help scan your brain in The Register. The paper author, Douwe Kiela (University of Cambridge), was quoted saying "In AI, especially unimodal AI, we frequently have much better datasets, so we felt we had to explain to the AI community why this dataset was comparatively smaller". The paper got social media traction with 197 shares. A user, @deviparikh, tweeted "An interesting (and important!) task that - from what we can tell so far - really requires both modalities to do the task well. Challenge with $100k prize money Starter code".
Leading researcher Aaron Courville (Université de Montréal) came out with "Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation" The researchers identify two key issues that limit such generalization.
The paper shared the most on social media this week is "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere" by Tongzhou Wang et al (May 2020) with 129 shares. The investigators identify two key properties related to the contrastive loss : (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere. @NeurAutomata (NeurAutomata) tweeted "karpathy "RT phillip_isola: Sharing two new preprints on the science and theory of contrastive learning: pdf: code: w/ YonglongT, Chen Sun, poolio, dilipkay, Cordelia Schmid pdf: code".
This week was very active for "Computer Science - Computers and Society", with 55 new papers.
The paper discussed most in the news over the past week was by a team at University of Oxford: "Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims" by Miles Brundage et al (Apr 2020), which was referenced 7 times, including in the article What to Do When AI Fails in O'Reilly Network. The paper author, Sarah Cameron, was quoted saying "It is an international effort with contributor experts from multiple countries, which is really encouraging for promoting a more united, less duplicative approach". The paper also got the most social media traction with 348 shares. A Twitter user, @Miles_Brundage, said "Super excited to share a report I’ve been working on with a bunch of colleagues from different orgs since last year. Building on a workshop last April, we analyze various ways to improve the verifiability of claims about AI systems. You can read it here".
Leading researcher Yoshua Bengio (Université de Montréal) published "COVI White Paper"
The paper shared the most on social media this week is "A socio-technical framework for digital contact tracing" by Ricardo Vinuesa et al (May 2020) with 256 shares. @RecklessCoding (Recklesscoding) tweeted "Our latest short paper discusses a framework on assessing contact tracing apps. We use three sample cases, including UK's NSHx app, Austria's Stopp, and Singapore's Tracetogether. We also compare our framework against the guidelines releases by the EDPB".
Over the past week, 19 new papers were published in "Computer Science - Human-Computer Interaction".
This week was very active for "Computer Science - Learning", with 359 new papers.
The paper discussed most in the news over the past week was "The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America" by Benjamin Charles Germain Lee et al (May 2020), which was referenced 12 times, including in the article Innovator Ben Lee and LC Labs Host “Data Jam” with 100 Million Historic Newspaper Images in Library of Congress. The paper author, Ben Lee, was quoted saying "I loved it because it emphasized the visual nature of the pages — seeing the visual diversity of the content coming out of the project, I just thought it was so cool, and I wondered what it would be like to chronicle content like this from all over America". The paper got social media traction with 206 shares. A Twitter user, @Psythor, said "AI has just automated a job I used to have 11 years ago. When I was doing my MA, I worked at in a news clippings department, which would analyse scans of local papers for mentions of companies etc. I’d spend 8 hours non-stop dragging boxes around different headlines and stories".
Leading researcher Yoshua Bengio (Université de Montréal) published "An Analysis of the Adaptation Speed of Causal Models".
The paper shared the most on social media this week is "Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere" by Tongzhou Wang et al (May 2020)
Over the past week, eight new papers were published in "Computer Science - Multiagent Systems".
The paper discussed most in the news over the past week was by a team at University of Southern California: "Lifelong Multi-Agent Path Finding in Large-Scale Warehouses" by Jiaoyang Li et al (May 2020), which was referenced 2 times, including in the article Amazon’s AI tool can plan collision-free paths for 1,000 warehouse robots in Venturebeat. The paper was shared 3 times in social media. The investigators study the lifelong variant of MAPF where agents are constantly engaged with new goal locations, such as in large - scale warehouses.
Over the past week, 19 new papers were published in "Computer Science - Neural and Evolutionary Computing".
This week was active for "Computer Science - Robotics", with 52 new papers.
The paper discussed most in the news over the past week was by a team at University of Southern California: "Lifelong Multi-Agent Path Finding in Large-Scale Warehouses" by Jiaoyang Li et al (May 2020)
The paper shared the most on social media this week is by a team at Google: "Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization" by Peter Karkus et al (May 2020) with 66 shares. @quantombone (Tomasz Malisiewicz) tweeted "Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization. A novel neural network architecture that learns a spatial view-embedding map. ArXiv: Project page: #robotics #computervision".