Week Ending 04.14.19
RESEARCH WATCH: 04.14.19
Over the past week, 293 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was "Discrimination through optimization: How Facebooks ad delivery can lead to skewed outcomes" by Muhammad Ali et al (Apr 2019), which was referenced 47 times, including in the article Facebook’s Ad System Might Be Hard-Coded for Discrimination in Wired News. The paper author, Alan Mislove (Computer science professor at Northeastern University), was quoted saying "All advertising is based on auctions all over the web, and I don’t know how you fix that without just saying we don’t have those kinds of ads". The paper also got the most social media traction with 1292 shares. The investigators demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platforms own predictions about the relevance of ads to different groups of users. A user, @TimKarr, tweeted "April 4, 2019: Academic paper published analyzing FB's algorithms to find they're built using historically discriminatory data. The algorithms deliver results biased against people based on race & gender, & perpetuate discrimination in advertising".
Leading researcher Yoshua Bengio (Université de Montréal) published "Speech Model Pre-training for End-to-End Spoken Language Understanding" @_josh_meyer_ tweeted "Good thread ---> inspirations of a dataset and the models trained on it".
The paper shared the most on social media this week is by a team at Google: "Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras" by Ariel Gordon et al (Apr 2019) with 361 shares. @gersart (Gerry Straathof) tweeted "This was what I was trying to do with a video shot out a window while driving. I wanted to find some way of understanding which elements could be removed, though, so I could make a dependable slitscan of the highway view. In mine, fenceposts and power poles were the issue".
Over the past week, 71 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was "Creating Pro-Level AI for Real-Time Fighting Game with Deep Reinforcement Learning" by Inseok Oh et al (Apr 2019), which was referenced 3 times, including in the article AI sketches cats, firetrucks, mosquitos, and yoga poses in Venturebeat. The paper got social media traction with 13 shares.
Leading researcher Yoshua Bengio (Université de Montréal) came out with "Reinforced Imitation in Heterogeneous Action Space" The investigators consider a challenging setting where an agent and an expert use different actions from each other. @gastronomy tweeted "> Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting w".
The paper shared the most on social media this week is by a team at Bar Ilan University: "Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation" by Amit Moryossef et al (Apr 2019) with 57 shares. @yoavgo ((((ل()(ل() 'yoav))))) tweeted "Our take on data to text generation: start with a verifiable planning stage to get the structure right. Then use neural generation to map into sentences. Very nice work by".
The most influential Twitter user discussing papers is Brian/ @RealScientists who shared "TESS Photometric Mapping of a Terrestrial Planet in the Habitable Zone: Detection of Clouds, Oceans, and Continents" by Rodrigo Luger et al (Mar 2019) and said: "On exoplanets - I am still trying to sort out this new paper using which states we create a "time-variable albedo map of the planet including persistent regions which we interpret as oceans and cloud banks indicative of continental features"".
This week was very active for "Computer Science - Computer Vision and Pattern Recognition", with 310 new papers.
The paper discussed most in the news over the past week was "Predictive Inequity in Object Detection" by Benjamin Wilson et al (Feb 2019), which was referenced 154 times, including in the article Apple’s Siri is as important as iPhone or the Mac in TechnologyNews.win. The paper author, Jamie Morgenstern (University of Pennsylvania), was quoted saying "The main takeaway from our work is that vision systems that share common structures to the ones we tested should be looked at more closely". The paper got social media traction with 169 shares. The investigators investigate whether state - of - the - art object detection systems have equitable predictive performance on pedestrians with different skin tones. A Twitter user, @defcon_5, observed "This is what systemic racism looks like.🚨🚨🚨 Black people may be at a greater risk of getting hit by self-driving cars because today's object-detection models exhibit higher precision on lighter skin tones #whitesupremacy".
Leading researcher Devi Parikh (Georgia Institute of Technology) came out with "Embodied Question Answering in Photorealistic Environments with Point Cloud Perception".
The paper shared the most on social media this week is by a team at Google: "Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras" by Ariel Gordon et al (Apr 2019) with 361 shares. @gersart (Gerry Straathof) tweeted "This was what I was trying to do with a video shot out a window while driving. I wanted to find some way of understanding which elements could be removed, though, so I could make a dependable slitscan of the highway view. In mine, fenceposts and power poles were the issue".
The most influential Twitter user discussing papers is Brian/ @RealScientists who shared "TESS Photometric Mapping of a Terrestrial Planet in the Habitable Zone: Detection of Clouds, Oceans, and Continents" by Rodrigo Luger et al (Mar 2019) and said: "On exoplanets - I am still trying to sort out this new paper using which states we create a "time-variable albedo map of the planet including persistent regions which we interpret as oceans and cloud banks indicative of continental features"".
Over the past week, 21 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was "Discrimination through optimization: How Facebooks ad delivery can lead to skewed outcomes" by Muhammad Ali et al (Apr 2019), which was referenced 47 times, including in the article Facebook’s Ad System Might Be Hard-Coded for Discrimination in Wired News. The paper author, Alan Mislove (Computer science professor at Northeastern University), was quoted saying "All advertising is based on auctions all over the web, and I don’t know how you fix that without just saying we don’t have those kinds of ads". The paper also got the most social media traction with 1292 shares. The researchers demonstrate that such skewed delivery occurs on Facebook, due to market and financial optimization effects as well as the platforms own predictions about the relevance of ads to different groups of users. A Twitter user, @TimKarr, said "April 4, 2019: Academic paper published analyzing FB's algorithms to find they're built using historically discriminatory data. The algorithms deliver results biased against people based on race & gender, & perpetuate discrimination in advertising".
This week was active for "Computer Science - Human-Computer Interaction", with 32 new papers.
The paper shared the most on social media this week is "Tea: A High-level Language and Runtime System for Automating Statistical Analysis" by Eunice Jun et al (Apr 2019) with 75 shares. @infrahumano (Javier Moreno 💚) tweeted "This paper explains what they are trying to do: (The repository does not seem to have any serious documentation, alas.)".
This week was active for "Computer Science - Learning", with 217 new papers.
The paper discussed most in the news over the past week was "Predictive Inequity in Object Detection" by Benjamin Wilson et al (Feb 2019), which was referenced 154 times, including in the article Apple’s Siri is as important as iPhone or the Mac in TechnologyNews.win. The paper author, Jamie Morgenstern (University of Pennsylvania), was quoted saying "The main takeaway from our work is that vision systems that share common structures to the ones we tested should be looked at more closely". The paper got social media traction with 169 shares. The researchers investigate whether state - of - the - art object detection systems have equitable predictive performance on pedestrians with different skin tones. On Twitter, @defcon_5 posted "This is what systemic racism looks like.🚨🚨🚨 Black people may be at a greater risk of getting hit by self-driving cars because today's object-detection models exhibit higher precision on lighter skin tones #whitesupremacy".
Leading researcher Yoshua Bengio (Université de Montréal) published "Speech Model Pre-training for End-to-End Spoken Language Understanding" @_josh_meyer_ tweeted "Good thread ---> inspirations of a dataset and the models trained on it".
The paper shared the most on social media this week is by a team at Google: "Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras" by Ariel Gordon et al (Apr 2019) with 361 shares. @gersart (Gerry Straathof) tweeted "This was what I was trying to do with a video shot out a window while driving. I wanted to find some way of understanding which elements could be removed, though, so I could make a dependable slitscan of the highway view. In mine, fenceposts and power poles were the issue".
Over the past week, 13 new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 31 new papers were published in "Computer Science - Neural and Evolutionary Computing".
This week was active for "Computer Science - Robotics", with 48 new papers.
Leading researcher Pieter Abbeel (University of California, Berkeley) published "Quasi-Direct Drive for Low-Cost Compliant Robotic Manipulation".
The paper shared the most on social media this week is by a team at Google: "Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras" by Ariel Gordon et al (Apr 2019) with 361 shares. @gersart (Gerry Straathof) tweeted "This was what I was trying to do with a video shot out a window while driving. I wanted to find some way of understanding which elements could be removed, though, so I could make a dependable slitscan of the highway view. In mine, fenceposts and power poles were the issue".
The most influential Twitter user discussing papers is Brian/ @RealScientists who shared "TESS Photometric Mapping of a Terrestrial Planet in the Habitable Zone: Detection of Clouds, Oceans, and Continents" by Rodrigo Luger et al (Mar 2019) and said: "On exoplanets - I am still trying to sort out this new paper using which states we create a "time-variable albedo map of the planet including persistent regions which we interpret as oceans and cloud banks indicative of continental features"".