Week Ending 7.26.2020
RESEARCH WATCH: 7.26.2020
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 "Iterative Effect-Size Bias in Ridehailing: Measuring Social Bias in Dynamic Pricing of 100 Million Rides" by Akshat Pandey et al (Jun 2020), which was referenced 14 times, including in the article Was your Uber, Lyft fare high because of algorithm bias? in USA Today. The paper author, Aylin Caliskan (George Washington University), was quoted saying "When machine learning is applied to social data, the algorithms learn the statistical regularities of the historical injustices and social biases embedded in these data sets". The paper got social media traction with 77 shares. The authors develop a random - effects based metric for the analysis of social bias in supervised machine learning prediction models where model outputs depend on U.S. locations. A Twitter user, @DavidZipper, posted "Analyzing 100 million Chicago ride hail trips, researchers found significant evidence of bias. Algorithms used by Uber/Lyft/Via led to higher fares for those going to neighborhoods with a high share of minority or older residents, for example. DL link".
The paper shared the most on social media this week is "Privacy-preserving Artificial Intelligence Techniques in Biomedicine" by Reihaneh Torkzadehmahani et al (Jul 2020) with 112 shares. The authors have led to several breakthroughs ranging from clinical decision support systems, image analysis to whole genome sequencing.
This week was very active for "Computer Science - Computer Vision and Pattern Recognition", with 309 new papers.
The paper discussed most in the news over the past week was by a team at Carnegie Mellon University: "Object Goal Navigation using Goal-Oriented Semantic Exploration" by Devendra Singh Chaplot et al (Jul 2020), which was referenced 10 times, including in the article Which way to the fridge? Common sense helps robots navigate in EurekAlert!. The paper author, Devendra S. Chaplot, was quoted saying "Common sense says that if you're looking for a refrigerator, you'd better go to the kitchen". The paper got social media traction with 114 shares. On Twitter, @dchaplot observed "Posted new paper on Object Goal Navigation describing our winning entry to the #CVPR2020 Habitat ObjectNav Challenge. Arxiv: Webpage: Our model also works in the real-world on a Locobot! 👇 w. A. Gupta".
Leading researcher Dhruv Batra (Georgia Institute of Technology) published "Spatially Aware Multimodal Transformers for TextVQA".
The paper shared the most on social media this week is by a team at Adobe: "Contact and Human Dynamics from Monocular Video" by Davis Rempe et al (Jul 2020) with 303 shares. The authors present a physics - based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input. @timsoret (Tim Soret) tweeted "Mind-blowing progress. A new neural network to generate motion data from ordinary 2D videos, with solid foot anchorage thanks to contact estimation refined by physics simulation".
This week was active for "Computer Science - Computers and Society", with 35 new papers.
The paper discussed most in the news over the past week was "Iterative Effect-Size Bias in Ridehailing: Measuring Social Bias in Dynamic Pricing of 100 Million Rides" by Akshat Pandey et al (Jun 2020)
This week was active for "Computer Science - Human-Computer Interaction", with 32 new papers.
This week was very active for "Computer Science - Learning", with 358 new papers.
The paper discussed most in the news over the past week was "Iterative Effect-Size Bias in Ridehailing: Measuring Social Bias in Dynamic Pricing of 100 Million Rides" by Akshat Pandey et al (Jun 2020)
Leading researcher Luc Van Gool (Computer Vision Laboratory) came out with "Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference".
The paper shared the most on social media this week is by a team at University of Tübingen: "Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding" by David Klindt et al (Jul 2020) with 208 shares.
Over the past week, 15 new papers were published in "Computer Science - Multiagent Systems".
The paper discussed most in the news over the past week was "Simulating COVID-19 in a University Environment" by Philip T. Gressman et al (Jun 2020), which was referenced 99 times, including in the article 5 Bigger and Better Ideas for Fall 2020 in Inside Higher Ed. The paper author, Jennifer Peck, was quoted saying "What we’ve shown is that if the social side is under control, you can manage the spread through academic contacts. So having said that, can you get the social side of the contacts under control? At this point, that’s a first-order question". The paper also got the most social media traction with 156 shares. A user, @DCBPhDV2, tweeted "Y'all. "In the absence of any intervention, all scenarios end with effectively all susceptible community members developing #COVID19 by the end of the semester, with peak infection rates reached between 20 and 40 days into the semester." #HigherEd".
Over the past week, 20 new papers were published in "Computer Science - Neural and Evolutionary Computing".
The paper discussed most in the news over the past week was "Fast and stable MAP-Elites in noisy domains using deep grids" by Manon Flageat et al (Jun 2020), which was referenced 1 time, including in the article Deep-Grid MAP-Elites: An algorithm to produce collections of diverse and high performing solutions in noisy domains in Tech Xplore. The paper author, Antoine Cully (Personal Robotics Laboratory), was quoted saying "Deep-Grid MAP-Elites uses specific mechanisms to add and remove solutions from the collections that allow the sub-population to progressively converge to the 'true' performance, without the need of multi-evaluations". The paper got social media traction with 18 shares. On Twitter, @CULLYAntoine posted "Glad to share our latest paper with introducing "Deed-Grid MAP-Elites" to improve the stability and convergence speed of MAP-Elites in noisy domains. To be presented at".
This week was very active for "Computer Science - Robotics", with 67 new papers.
The paper discussed most in the news over the past week was by a team at Carnegie Mellon University: "Object Goal Navigation using Goal-Oriented Semantic Exploration" by Devendra Singh Chaplot et al (Jul 2020)
Leading researcher Dhruv Batra (Georgia Institute of Technology) published "Seeing the Un-Scene: Learning Amodal Semantic Maps for Room Navigation".
The paper shared the most on social media this week is "ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM" by Carlos Campos et al (Jul 2020) with 198 shares. The authors present ORB - SLAM3, the first system able to perform visual, visual - inertial and multi - map SLAM with monocular, stereo and RGB - D cameras, using pin - hole and fisheye lens models. @mihaibujanca (Mihai Bujanca) tweeted "ORB-SLAM3 is bringing together the many of the contributions since the publication of ORB-SLAM2! Looking forward to seeing the results on the #UZH #droneracing dataset too! Also looks promising for #lifelong #SLAM".