Week Ending 4.11.2021
RESEARCH WATCH: 4.11.2021
This week was active for "Computer Science", with 1,170 new papers.
The paper discussed most in the news over the past week was "Variational inference with a quantum computer" by Marcello Benedetti et al (Mar 2021), which was referenced 63 times, including in the article Cambridge Quantum Computing Pioneers Quantum Machine Learning Methods for Reasoning in InsideBIGDATA. The paper author, Matthias Rosenkranz, was quoted saying "cannot offer simple explanations for their answers and struggle when asked how confident they are on certain possible outcomes". The paper got social media traction with 17 shares. A Twitter user, @rosenkranz, commented "Our new paper "Variational inference with a #quantum computer" has been out for a few days 🎉. We develop the methods, then demonstrate them using a few graphical models (e.g. hidden Markov). #QuantumComputing #MachineLearning".
Leading researcher Yoshua Bengio (Université de Montréal) published "hBert + BiasCorp -- Fighting Racism on the Web".
The paper shared the most on social media this week is by a team at Google: "Does Your Dermatology Classifier Know What It Doesnt Know? Detecting the Long-Tail of Unseen Conditions" by Abhijit Guha Roy et al (Apr 2021) with 130 shares. @xamat (Xavier Amatriain) tweeted "Happy to see the authors cite one of our two very relevant papers for this line of work. The other one is here".
This week was very active for "Computer Science - Artificial Intelligence", with 173 new papers.
The paper discussed most in the news over the past week was by a team at Université de Montréal: "Towards Causal Representation Learning" by Bernhard Schölkopf et al (Feb 2021), which was referenced 9 times, including in the article Why machine learning struggles with causality in KDNuggets. The paper got social media traction with 467 shares. A Twitter user, @NalKalchbrenner, posted "Causality in ML is one of those slippery concepts that are hard to get a good grip on - a bit like the concepts of consciousness and perhaps truth. This paper makes an attempt 👇", while @YisongMiao posted "Haven't read, seems like very interesting! RT for self-arxiv. Thanks!".
Leading researcher Pieter Abbeel (University of California, Berkeley) published "GEM: Group Enhanced Model for Learning Dynamical Control Systems" The authors take advantage of these structures to build effective dynamical models that are amenable to sample - based learning.
The paper shared the most on social media this week is by a team at MIT Computer Science and Artificial Intelligence Laboratory: "AST: Audio Spectrogram Transformer" by Yuan Gong et al (Apr 2021) with 95 shares. @yieldthought (Mark) tweeted "10. I should have read concurrent work mentioned above more closely ( Many differences: patch overlap (we have none), ImageNet pretrain (we don’t) embeddings (we learn), distillation (we do). Joint ablation would be fun!".
This week was very active for "Computer Science - Computer Vision and Pattern Recognition", with 348 new papers.
The paper discussed most in the news over the past week was by a team at OpenAI: "Learning Transferable Visual Models From Natural Language Supervision" by Alec Radford et al (Feb 2021), which was referenced 2 times, including in the article Simple Implementation of OpenAI CLIP model: A Tutorial in Towards Data Science. The paper got social media traction with 25 shares. A Twitter user, @haltakov, commented "Interesting resources if you want to learn more about CLIP network. ▪️ Blog: ▪️ Paper: ▪️ Code: ▪️ Multimodal neurons: ▪️ Search photos on Unsplash".
Leading researcher Dhruv Batra (Georgia Institute of Technology) came out with "Auxiliary Tasks and Exploration Enable ObjectNav".
The paper shared the most on social media this week is by a team at Google: "Does Your Dermatology Classifier Know What It Doesnt Know? Detecting the Long-Tail of Unseen Conditions" by Abhijit Guha Roy et al (Apr 2021)
This week was active for "Computer Science - Computers and Society", with 32 new papers.
This week was active for "Computer Science - Human-Computer Interaction", with 34 new papers.
This week was very active for "Computer Science - Learning", with 383 new papers.
The paper discussed most in the news over the past week was "Variational inference with a quantum computer" by Marcello Benedetti et al (Mar 2021)
Leading researcher Ruslan Salakhutdinov (Carnegie Mellon University) came out with "Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation".
The paper shared the most on social media this week is by a team at Google: "Does Your Dermatology Classifier Know What It Doesnt Know? Detecting the Long-Tail of Unseen Conditions" by Abhijit Guha Roy et al (Apr 2021)
Over the past week, 14 new papers were published in "Computer Science - Multiagent Systems".
The paper shared the most on social media this week is "Scaling Scaling Laws with Board Games" by Andrew L. Jones (Apr 2021) with 104 shares. @Inoryy (Roman Ring) tweeted "Scaling Laws seem to be even more generally applicable! I've seen this work unfold from a simple idea to a great paper it is today. It stands on its own merit but doubly so as Andy did it all as an independent researcher!".
Over the past week, 17 new papers were published in "Computer Science - Neural and Evolutionary Computing".
This week was very active for "Computer Science - Robotics", with 72 new papers.
The paper discussed most in the news over the past week was "Robotic Guide Dog: Leading a Human with Leash-Guided Hybrid Physical Interaction" by Anxing Xiao et al (Mar 2021), which was referenced 7 times, including in the article A laser equipped robotic guide dog to lead people who are visually impaired in Tech Xplore. The paper was shared 3 times in social media. A Twitter user, @junzengx14, observed "Glad to share our recent work to use quadrupedal robot to better serve visually impaired people. Huge thanks to and check our ICRA paper for details".
Leading researcher Dhruv Batra (Georgia Institute of Technology) published "Auxiliary Tasks and Exploration Enable ObjectNav".