Week Ending 9.19.2021
RESEARCH WATCH: 9.19.2021
This week was active for "Computer Science", with 1,396 new papers.
The paper discussed most in the news over the past week was by a team at Stanford University: "On the Opportunities and Risks of Foundation Models" by Rishi Bommasani et al (Aug 2021), which was referenced 13 times, including in the article Best of arXiv.org for AI, Machine Learning, and Deep Learning – August 2021 in InsideBIGDATA. The paper author, Rishi Bommasani, was quoted saying "The commercial incentive can lead companies to ignore social externalities such as the technological displacement of labor, the health of an informational ecosystem required for democracy, the environmental cost of computing resources, and the profit-driven sale of technologies to non-democratic regimes".
Leading researcher Kyunghyun Cho (New York University) published "Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery".
This week was extremely active for "Computer Science - Artificial Intelligence", with 265 new papers.
The paper discussed most in the news over the past week was by a team at Stanford University: "On the Opportunities and Risks of Foundation Models" by Rishi Bommasani et al (Aug 2021)
Leading researcher Sergey Levine (University of California, Berkeley) came out with "Conservative Data Sharing for Multi-Task Offline Reinforcement Learning".
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 by a team at University at Albany: "Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces" by Hui Guo et al (Aug 2021), which was referenced 5 times, including in the article Pupils reveal a liar Science in Tek Deeps. The paper author, Hui Guo (University of New South Wales), was quoted saying "This phenomenon is caused by the lack of physiological constraints in the GAN models".
Leading researcher Kyunghyun Cho (New York University) published "Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery".
This week was active for "Computer Science - Computers and Society", with 39 new papers.
The paper discussed most in the news over the past week was by a team at Stanford University: "On the Opportunities and Risks of Foundation Models" by Rishi Bommasani et al (Aug 2021)
This week was active for "Computer Science - Human-Computer Interaction", with 33 new papers.
The paper discussed most in the news over the past week was "AugLimb: Compact Robotic Limb for Human Augmentation" by Zeyu Ding et al (Aug 2021), which was referenced 3 times, including in the article AugLimb: A compact robotic limb to support humans during everyday activities in Tech Xplore. The paper author, Haoran Xie (Japan Advanced Institute of Science and Technology), was quoted saying "In our future studies, we would like to explore effective ways to control AugLimb with biological information, such as electromyography (EMG) signals from muscles or electroencephalogram (EEG) techniques that detect brain waves".
This week was very active for "Computer Science - Learning", with 426 new papers.
The paper discussed most in the news over the past week was by a team at Stanford University: "On the Opportunities and Risks of Foundation Models" by Rishi Bommasani et al (Aug 2021)
Leading researcher Kyunghyun Cho (New York University) published "Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery".
Over the past week, 18 new papers were published in "Computer Science - Multiagent Systems".
Over the past week, 22 new papers were published in "Computer Science - Neural and Evolutionary Computing".
Leading researcher Quoc V. Le (Google) came out with "Primer: Searching for Efficient Transformers for Language Modeling".
This week was extremely active for "Computer Science - Robotics", with 157 new papers.
The paper discussed most in the news over the past week was "AugLimb: Compact Robotic Limb for Human Augmentation" by Zeyu Ding et al (Aug 2021)
Leading researcher Sergey Levine (University of California, Berkeley) came out with "Conservative Data Sharing for Multi-Task Offline Reinforcement Learning".