Week Ending 05.26.19
RESEARCH WATCH: 05.26.19
Over the past week, 915 new papers were published in "Computer Science".
The paper discussed most in the news over the past week was by a team at Samsung: "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models" by Egor Zakharov et al (May 2019), which was referenced 146 times, including in the article Mona Lisa 'brought to life' with deepfake AI in BBC. The paper author, Egor Zakharov(Samsung), was quoted saying "Effectively, the learned model serves as a realistic avatar of a person". The paper also got the most social media traction with 38314 shares. A user, @catovitch, tweeted "I wonder if this can/will be used with video compression. If it can be done in real time, the decoder could just be told "build a face our of this frame, then rotate it to these 3D positions for the next 5 frames"".
Leading researcher Yoshua Bengio (Université de Montréal) published "The Journey is the Reward: Unsupervised Learning of Influential Trajectories" @gastronomy tweeted "> Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. T".
Over the past week, 89 new papers were published in "Computer Science - Artificial Intelligence".
The paper discussed most in the news over the past week was "Survival of the Fittest in PlayerUnknown BattleGround" by Brij Rokad et al (May 2019), which was referenced 8 times, including in the article AI Predicts Survival of The Fittest in PUBG in IGN India. The paper got social media traction with 6 shares.
Leading researcher Yoshua Bengio (Université de Montréal) came out with "The Journey is the Reward: Unsupervised Learning of Influential Trajectories" @gastronomy tweeted "> Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. T".
The paper shared the most on social media this week is by a team at Rensselaer Polytechnic Institute: "PaperRobot: Incremental Draft Generation of Scientific Ideas" by Qingyun Wang et al (May 2019) with 228 shares. The investigators present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human - written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory - attention networks : from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow - on paper. @mandubian (mandubianhotep) tweeted ""60% of 6.4 million papers in biomedic & chemistry are about incremental work" Paperbot builds graph knowledge from existing papers, then generate new incremental ideas and their abstract/conclusion and finally title for future follow-on papers... Whoaa :D".
Over the past week, 166 new papers were published in "Computer Science - Computer Vision and Pattern Recognition".
The paper discussed most in the news over the past week was by a team at Samsung: "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models" by Egor Zakharov et al (May 2019), which was referenced 146 times, including in the article Mona Lisa 'brought to life' with deepfake AI in BBC. The paper author, Egor Zakharov(Samsung), was quoted saying "Effectively, the learned model serves as a realistic avatar of a person". The paper also got the most social media traction with 38316 shares. A user, @catovitch, tweeted "I wonder if this can/will be used with video compression. If it can be done in real time, the decoder could just be told "build a face our of this frame, then rotate it to these 3D positions for the next 5 frames"".
Leading researcher Luc Van Gool (Computer Vision Laboratory) published "Semi-Supervised Learning by Augmented Distribution Alignment" The authors propose a simple yet effective semi - supervised learning approach called Augmented Distribution Alignment.
Over the past week, 20 new papers were published in "Computer Science - Computers and Society".
The paper discussed most in the news over the past week was "Integrating Artificial Intelligence into Weapon Systems" by Philip Feldman et al (May 2019), which was referenced 6 times, including in the article Research indicates the only defense against killer AI is not developing it in The Next Web. The paper got social media traction with 23 shares. A Twitter user, @jasondmoss, posted "You'd have to be deluded to think that if #Google or #Microsoft employees refuse to do something, it won't get built. #AI #WeaponsSystems #Autonomy #Inevitable #Newsletter".
Over the past week, 20 new papers were published in "Computer Science - Human-Computer Interaction".
The paper discussed most in the news over the past week was by a team at University of Pennsylvania: "What Twitter Profile and Posted Images Reveal About Depression and Anxiety" by Sharath Chandra Guntuku et al (Apr 2019), which was referenced 23 times, including in the article Twitter photos may help detect users with depression, anxiety in The Inquirer. The paper author, Sharath Guntuku, was quoted saying "It is challenging to transform pixels that form the images to interpretable features, but with the advances in computer vision algorithms, we are now attempting to uncover another dimension of the condition as it manifests online." The paper got social media traction with 9 shares. The investigators examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users.
This week was very active for "Computer Science - Learning", with 372 new papers.
The paper discussed most in the news over the past week was by a team at Samsung: "Few-Shot Adversarial Learning of Realistic Neural Talking Head Models" by Egor Zakharov et al (May 2019), which was referenced 146 times, including in the article Mona Lisa 'brought to life' with deepfake AI in BBC. The paper author, Egor Zakharov(Samsung), was quoted saying "Effectively, the learned model serves as a realistic avatar of a person". The paper also got the most social media traction with 38321 shares. A user, @catovitch, tweeted "I wonder if this can/will be used with video compression. If it can be done in real time, the decoder could just be told "build a face our of this frame, then rotate it to these 3D positions for the next 5 frames"".
Leading researcher Yoshua Bengio (Université de Montréal) came out with "The Journey is the Reward: Unsupervised Learning of Influential Trajectories" @gastronomy tweeted "> Unsupervised exploration and representation learning become increasingly important when learning in diverse and sparse environments. T".
Over the past week, nine 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.
The paper discussed most in the news over the past week was "Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped" by Nathan Kau et al (May 2019), which was referenced 30 times, including in the article Stanford’s Doggo is a petite robotic quadruped you can (maybe) build yourself in TechCrunch. The paper author, Nathan Kau, was quoted saying "We had seen these other quadruped robots used in research, but they weren’t something that you could bring into your own lab and use for your own projects". The paper got social media traction with 17 shares. The researchers present Stanford Doggo, a quasi - direct - drive quadruped capable of dynamic locomotion.
The paper shared the most on social media this week is by a team at University of Vermont: "Automated shapeshifting for function recovery in damaged robots" by Sam Kriegman et al (May 2019) with 69 shares. @Gary_An (Gary An) tweeted "This is fascinating and super-cool!".