Week Ending 08.04.19
RESEARCH WATCH: 08.04.19
Over the past week, 842 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was "Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites" by Elena Maris et al (Jul 2019), which was referenced 197 times, including in the article Four short links: 31 July 2019 in O'Reilly Network. The paper author, Elena Maris (Postdoctoral researcher at Microsoft), was quoted saying "The fact that the mechanism for adult site tracking is so similar to, say, online retail should be a huge red flag. This isn’t picking out a sweater and seeing it follow you across the web. This is so much more specific and deeply personal". The paper got social media traction with 167 shares. The authors explore tracking and privacy risks on pornography websites. A Twitter user, @citadelo, posted "This research focuses on porn-sites users' tracking: "analysis of 22,484 pornography websites indicated that 93% leak user data to a third party." Unfortunately even incognito window does not solve the entire problem. Another reason to use".
Leading researcher Kyunghyun Cho (New York University) came out with "Improving localization-based approaches for breast cancer screening exam classification".
The paper shared the most on social media this week is by a team at Google: "On Mutual Information Maximization for Representation Learning" by Michael Tschannen et al (Jul 2019) with 104 shares. @LiamFedus (William Fedus) tweeted "Brain Zurich group following up careful scrutiny of disentangled representations with another thoughtful research piece on mutual information (MI) and representation learning. MI may not be sufficient for learning strong reps; looser bounds can even result in better reps; Nice!".
Over the past week, 50 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "Tackling Climate Change with Machine Learning" by David Rolnick et al (Jun 2019), which was referenced 28 times, including in the article Commentary: Applying Machine Learning To Improve The Supply Chain in Benzinga.com. The paper author, David Rolnick (Massachusetts Institute of Technology), was quoted saying "Climate change does not present one problem, it presents multiple problems. AI is only one of the tools that can have an impact in the fight to mitigate the effects of climate change". The paper also got the most social media traction with 1494 shares. The researchers describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. On Twitter, @ikdeepl said "Yikes “In 2006, at least two Scottish seafood firms flew hundreds of metric tons of shrimp from Scotland to China and Thailand for peeling, then back to Scotland for sale – because they could save on labor costs”".
Leading researcher Ruslan Salakhutdinov (Carnegie Mellon University) came out with "MineRL: A Large-Scale Dataset of Minecraft Demonstrations" @gastronomy tweeted "> The sample inefficiency of standard deep reinforcement learning methods precludes their application to many real-world problems. Methods which leverage h".
This week was active for "Computer Science - Computer Vision and Pattern Recognition", with 215 new papers.
The paper discussed most in the news over the past week was by a team at UC Berkeley: "Natural Adversarial Examples" by Dan Hendrycks et al (Jul 2019), which was referenced 18 times, including in the article Learning from adversaries in O'Reilly Network. The paper author, Steven Basart, was quoted saying "Anyone willing to test their models against our data set is free to do so". The paper got social media traction with 536 shares. On Twitter, @DanHendrycks said "Natural Adversarial Examples are real-world and unmodified examples which cause classifiers to be consistently confused. The new dataset has 7,500 images, which we personally labeled over several months. Paper: Dataset and code".
Leading researcher Kyunghyun Cho (New York University) published "Improving localization-based approaches for breast cancer screening exam classification".
Over the past week, 19 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was "Tracking sex: The implications of widespread sexual data leakage and tracking on porn websites" by Elena Maris et al (Jul 2019)
This week was extremely active for "Computer Science - Human-Computer Interaction", with 66 new papers.
The paper discussed most in the news over the past week was "GLTR: Statistical Detection and Visualization of Generated Text" by Sebastian Gehrmann et al (Jun 2019), which was referenced 16 times, including in the article AI now can spot fake news generated by AI in CNET News. The paper author, Sebastian Gehrmann, was quoted saying "Someone with enough computing power could automatically generate thousands of websites with real looking text about any topic of their choice. While we have not quite arrived at this point of the focused generation yet, large language models can already generate text that is indistinguishable from human-written text." The paper got social media traction with 45 shares. A user, @PeteDaGuru, tweeted "Detecting fake text - #ML #NLP live at - Giant Language Model Test Room GLTR - Statistical Detection and Visualization of Generated Text (from MIT-IBM Watson AI Lab and HarvardNLP 2019-07-10)".
This week was active for "Computer Science - Learning", with 237 new papers.
The paper discussed most in the news over the past week was by a team at Massachusetts Institute of Technology: "Tackling Climate Change with Machine Learning" by David Rolnick et al (Jun 2019)
Leading researcher Kyunghyun Cho (New York University) published "Screening Mammogram Classification with Prior Exams".
The paper shared the most on social media this week is by a team at Google: "On Mutual Information Maximization for Representation Learning" by Michael Tschannen et al (Jul 2019)
Over the past week, three new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 28 new papers were published in "Computer Science - Neural and Evolutionary Computing".
The paper discussed most in the news over the past week was "Lookahead Optimizer: k steps forward, 1 step back" by Michael R. Zhang et al (Jul 2019), which was referenced 1 time, including in the article Waymo Teams Up With DeepMind; Lyft Shares Large-Scale Dataset in SyncedReview.com. The paper got social media traction with 99 shares. The authors propose a new optimization algorithm, Lookahead, that is orthogonal to these previous approaches and iteratively updates two sets of weights. A user, @hillbig, tweeted "New SGD algorithm "Lookahead" updates "fast" weight k times and then updates "slow" weight once in the direction of the final fast weights. Significantly improve the performance of SGD and make it more robust against hyperparameter changes".
Leading researcher Ruslan Salakhutdinov (Carnegie Mellon University) came out with "MineRL: A Large-Scale Dataset of Minecraft Demonstrations"
Over the past week, 33 new papers were published in "Computer Science - Robotics".
The paper discussed most in the news over the past week was "Learning to Solve a Rubiks Cube with a Dexterous Hand" by Tingguang Li et al (Jul 2019), which was referenced 3 times, including in the article AI researchers test a robot's dexterity by handing it a Rubik's cube | VentureBeat in Venturebeat. The paper got social media traction with 8 shares.