Week Ending 5.17.2020
RESEARCH WATCH: 5.17.2020
This week was active for "Computer Science - Artificial Intelligence", with 101 new papers.
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 70 times, including in the article Western countries want to reopen. Renewed outbreaks in China and South Korea show the continued risk in CNN. 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 2498 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 commented "> We present two models for the COVID-19 pandemic predicting the impact of u".
Leading researcher Pieter Abbeel (University of California, Berkeley) published "Planning to Explore via Self-Supervised World Models".
The paper shared the most on social media this week is "The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes" by Douwe Kiela et al (May 2020) with 188 shares. @deviparikh (Devi Parikh) 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".
The most influential Twitter user discussing papers is Edward Grefenstette who shared "On the State of the Art of Evaluation in Neural Language Models" by Gábor Melis et al (Jul 2017) and said: "Love this. Similar result (different domain) to Melis et al 2017: That paper was pearls before swine for the EMNLP reviewers that read it. Also echos stuff that has observed, I believe". Note that this paper was published about two years ago.
Over the past week, 190 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".
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 13 times, including in the article Facebook says AI has a ways to go to detect nasty memes in ZDNet. 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 188 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 Pieter Abbeel (University of California, Berkeley)
This week was active for "Computer Science - Computers and Society", with 42 new papers.
The paper discussed most in the news over the past week was by a team at Johns Hopkins University: "A County-level Dataset for Informing the United States Response to COVID-19" by Benjamin D. Killeen et al (Apr 2020), which was referenced 2 times, including in the article Strong Social Distancing Measures In The United States Reduced The COVID-19 Growth Rate in Health Affairs. The paper got social media traction with 92 shares. A Twitter user, @MathiasUnberath, said "While the spike in grocery store visits indeed immediately follows the declaration of a national emergency, there might be other reasons? We are investigating. Thank you for empowering the research community with your data. #COVIDー19 #COVID2019".
Over the past week, 19 new papers were published in "Computer Science - Human-Computer Interaction".
The paper discussed most in the news over the past week was by a team at Georgia Institute of Technology: "CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization" by Zijie J. Wang et al (Apr 2020), which was referenced 1 time, including in the article Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2020 in InsideBIGDATA. The paper also got the most social media traction with 512 shares. A Twitter user, @fabtar, posted "This is very cool. Looking forward to test it 👏🚀", while @nicolangnl commented "First there was the playground for neural networks: Now there is this great tool for learning and teaching the concept of CNNs. Well done!".
Leading researcher Devi Parikh (Georgia Institute of Technology) came out with "Exploring Crowd Co-creation Scenarios for Sketches".
This week was very active for "Computer Science - Learning", with 327 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 11 times, including in the article 'Millions of Historic Newspaper Images Get the Machine Learning Treatment at the Library of Congress' by Devin Coldewey in Information Today. 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 201 shares. A user, @Psythor, tweeted "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 Pieter Abbeel (University of California, Berkeley) came out with "Planning to Explore via Self-Supervised World Models".
The paper shared the most on social media this week is "Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis" by Rafael Valle et al (May 2020) with 132 shares. The researchers propose Flowtron : an autoregressive flow - based generative network for text - to - speech synthesis with control over speech variation and style transfer. @PyTorch (PyTorch) tweeted "FlowTron: Improved Text to Speech Engine from NVIDIA Try it out now! Paper: Code".
The most influential Twitter user discussing papers is Edward Grefenstette who shared "On the State of the Art of Evaluation in Neural Language Models" by Gábor Melis et al (Jul 2017)
Over the past week, 11 new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 33 new papers were published in "Computer Science - Neural and Evolutionary Computing".
The paper discussed most in the news over the past week was by a team at UC Berkeley: "NBDT: Neural-Backed Decision Trees" by Alvin Wan et al (Apr 2020), which was referenced 3 times, including in the article Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2020 in InsideBIGDATA. The paper got social media traction with 20 shares.
Leading researcher Pieter Abbeel (University of California, Berkeley)
The paper shared the most on social media this week is by a team at Tel Aviv University: "Implicit Regularization in Deep Learning May Not Be Explainable by Norms" by Noam Razin et al (May 2020) with 59 shares. @TheGradient (Hossein Mobahi) tweeted "Interesting work showing that implicit regularization of gradient based optimizers can't be modeled by penalizing any type of norm (providing matrix factorization examples that any norm goes to ∞). The study speculates the right path might be penalizing rank instead of norm".
This week was active for "Computer Science - Robotics", with 60 new papers.
The paper discussed most in the news over the past week was by a team at Google: "Thinking While Moving: Deep Reinforcement Learning with Concurrent Control" by Ted Xiao et al (Apr 2020), which was referenced 3 times, including in the article DRL Helps Robots ‘Think While Moving’ in SyncedReview.com. The paper got social media traction with 153 shares. A Twitter user, @ericjang11, posted "Check out our work on making robots grasp faster by planning concurrently while moving! Grasping in 50% less time! 💵💵", while @JeffDean said "Some very nice work from our robotics research team enables robots to get a faster grasp on the problem at hand by contemplating and doing at the same time. Up to 50% faster grasping!".
Leading researcher Pieter Abbeel (University of California, Berkeley) came out with "Planning to Explore via Self-Supervised World Models".