Week Ending 8.9.2020
RESEARCH WATCH: 8.9.2020
Over the past week, 90 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was "Predicting job-hopping likelihood using answers to open-ended interview questions" by Madhura Jayaratne et al (Jul 2020), which was referenced 7 times, including in the article This AI Model Can Predict If You Are A Job Hopper Or Not in Analytics India Magazine. The paper got social media traction with 7 shares. The authors show that the language one uses when responding to interview questions related to situational judgment and past behaviour is predictive of their likelihood to job hop. On Twitter, @mkaplanPMP commented "Frequent movement from job to job is found to be associated with one's personality. This paper presents "a novel approach to predicting job-hopping likelihood using answers to typical interview questions related to past behavior and situational judgment."".
Leading researcher Pieter Abbeel (UC Berkeley) published "Dynamics Generalization via Information Bottleneck in Deep Reinforcement Learning".
The paper shared the most on social media this week is by a team at University College London: "Question and Answer Test-Train Overlap in Open-Domain Question Answering Datasets" by Patrick Lewis et al (Aug 2020) with 237 shares. The researchers perform a detailed study of the test sets of three popular open - domain benchmark datasets with respect to these competencies. @barbara_plank (Barbara Plank) tweeted "woah π²! 60% of overlap and 30% close-paraphrases is extreme... from the paper "a greater emphasis should be placed on more behaviour-driven evaluation, rather than pursuing single-number overall accuracy figures." - yes! totally agree #beyondaccuracy".
The most influential Twitter user discussing papers is ππ΄π’π―π« who shared "Towards a Human-like Open-Domain Chatbot" by Daniel Adiwardana et al (Jan 2020) and said: "I thought they did: What does the 79% SSA human-rating and the tuning of best-of refer to if not that?".
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 270 new papers.
The paper discussed most in the news over the past week was "Conditional Image Retrieval" by Mark Hamilton et al (Jul 2020), which was referenced 5 times, including in the article Researchers develop an artificial intelligence that can spot βhidden connectionsβ between paintings in ReadSector. The paper got social media traction with 12 shares. A user, @MarkGhuneim, tweeted "Conditional Image Retrieval - using machine learning to explore shared semantic content between works of art of vastly different media and cultural origin".
The paper shared the most on social media this week is by a team at Google: "NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections" by Ricardo Martin-Brualla et al (Aug 2020) with 147 shares. @jbhuang0604 (Jia-Bin Huang) tweeted "Amazing results! Seems like NeRF has been around for ages, but itβs actually not been published at ECCV yet".
The most influential Twitter user discussing papers is ππ΄π’π―π« who shared "Towards a Human-like Open-Domain Chatbot" by Daniel Adiwardana et al (Jan 2020)
Over the past week, 29 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was by a team at University of Colorado Boulder: "Analyzing Twitter Users Behavior Before and After Contact by the Internet Research Agency" by Upasana Dutta et al (Aug 2020), which was referenced 4 times, including in the article Twitter users may have changed their behavior after contact with Russian trolls in Mirage News. The paper author, Shivakant Mishra, was quoted saying "We said βsure, there are all these bots and they are spreading misinformation,'". The paper got social media traction with 13 shares. A user, @MattGrossmann, tweeted "Twitter users contacted by Russian IRA in 2016 changed their subsequent online political behavior #SocSciResearch".
The paper shared the most on social media this week is by a team at The University of Vermont: "Generalized Word Shift Graphs: A Method for Visualizing and Explaining Pairwise Comparisons Between Texts" by Ryan J. Gallagher et al (Aug 2020) with 153 shares. @LauraK_Nelson (Laura Nelson) tweeted "As my colleagues , and I are deep in the midst of looking at the words underneath computational measures, I fully endorse the ultimate tweet in this thread: π always π look π at π the π words!".
The most influential Twitter user discussing papers is ππ΄π’π―π« who shared "Towards a Human-like Open-Domain Chatbot" by Daniel Adiwardana et al (Jan 2020)
This week was active for "Computer Science - Human-Computer Interaction", with 31 new papers.
The paper discussed most in the news over the past week was by a team at University of Colorado Boulder: "Analyzing Twitter Users Behavior Before and After Contact by the Internet Research Agency" by Upasana Dutta et al (Aug 2020)
Leading researcher Sergey Levine (University of California, Berkeley) came out with "Assisted Perception: Optimizing Observations to Communicate State".
This week was very active for "Computer Science - Learning", with 325 new papers.
The paper discussed most in the news over the past week was "The Computational Limits of Deep Learning" by Neil C. Thompson et al (Jul 2020), which was referenced 10 times, including in the article Shrinking deep learningβs carbon footprint in Mirage News. The paper author, Neil Thompson, was quoted saying "You have to throw a lot more computation at something to get a little improvement in performance". The paper got social media traction with 331 shares. A user, @DataScienceNIG, tweeted "Is Deep Learning reaching a computational limit? Maybe yes, check a work by team based on a review of 1,058 research papers, spanning 5 prominent application areas conducting computation per network pass & hardware burden analysis. More".
Leading researcher Pieter Abbeel (UC Berkeley) published "Robust Reinforcement Learning using Adversarial Populations".
The paper shared the most on social media this week is by a team at University of Minnesota: "The world as a neural network" by Vitaly Vanchurin (Aug 2020) with 541 shares. @badr_nlp (Badr Abdullah πͺ πΎπͺ) tweeted "And 2020 is just a bad local minima! SGD is the one to blame".
The most influential Twitter user discussing papers is ππ΄π’π―π« who shared "Towards a Human-like Open-Domain Chatbot" by Daniel Adiwardana et al (Jan 2020)
Over the past week, eight new papers were published in "Computer Science - Multiagent Systems".
Leading researcher Pieter Abbeel (UC Berkeley) published "Robust Reinforcement Learning using Adversarial Populations". This paper was also shared the most on social media with 57 tweets.
Over the past week, 19 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 National University of Defense Technology: "AReLU: Attention-based Rectified Linear Unit" by Dengsheng Chen et al (Jun 2020), which was referenced 1 time, including in the article Understanding of ARELU (Attention-based Rectified Linear Unit) in Towards Data Science. The paper got social media traction with 5 shares.
This week was very active for "Computer Science - Robotics", with 71 new papers.
The paper discussed most in the news over the past week was by a team at Zhejiang University: "Event-based Stereo Visual Odometry" by Yi Zhou et al (Jul 2020), which was referenced 1 time, including in the article Video Friday: Japan's Giant Gundam Robot Is Nearly Complete in Spectrum Online. The paper got social media traction with 15 shares.
Leading researcher Pieter Abbeel (UC Berkeley) published "Robust Reinforcement Learning using Adversarial Populations". This paper was also shared the most on social media with 57 tweets.