AI Technology & Industry Review
Stanford researchers’ DERL (Deep Evolutionary Reinforcement Learning) is a novel computational framework that enables AI agents to evolve morphologies and learn challenging locomotion and manipulation tasks in complex environments using only low level egocentric sensory information.
Is it possible for machines to understand and integrate human emotions in visual arts? Meet ArtEmis, a new large-scale dataset of emotional reactions and explanations for visual artworks.
In a new paper, Stanford University researchers dispel the assumption that boosting deep learning model performance on ImageNet translates to better performance on medical imaging tasks.
Researcher from Toyota Research Institute announce joint efforts with Stanford’s Dynamic Design Lab, to research on automotive safety improvements.
WILDS is an ambitious benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications.
Researchers from Stanford University, UC Berkeley and CMU review neuromechanical simulations and DRL, with a focus on modelling the control of human locomotion.
The researchers introduce sinusoidal representation networks (SIRENs) as a method for leveraging periodic activation functions for implicit neural representations.
The new goal of the Folding@home project is modelling the structure of SARS-CoV-2 spike protein to identify sites that can be targeted by a therapeutic antibody.
A team of researchers from Institut de Robòtica i Informàtica Industrial and Harvard University recently introduced 3DPeople, a large-scale comprehensive dataset with specific geometric shapes of clothes that is suitable for many computer vision tasks involving clothed humans.
To enable both content creators and end users to seriously restyle their apps’ interfaces while maintaining content detail clarity essential to their usability, researchers from Stanford have proposed ImagineNet, a novel and powerful new tool for interface customisation.
Snorkel Drybell, an experimental internal system which leverages the open-sourced Snorkel framework to harness various existing organizational knowledge resources and generate training data for web-scale machine learning models.
Last October Stanford University announced plans to create an institute built for artificial intelligence research and development. Today, the school made good on its pledge, launching the Stanford Institute for Human-Centered Artificial Intelligence (Stanford HAI) with a mission “to advance AI research, education, policy, and practice to improve the human condition.”
The International Conference on Machine Learning (ICML) 2018 will be held July 10 – 15 in Stockholm, Sweden. Yesterday, from more than 600 accepted papers, the prestigious conference announced its Best Paper Awards.
The 2018 Conference on Computer Vision and Pattern Recognition (CVPR) opened today in Salt Lake City, USA. The CVPR organizing committee used the occasion to announce its coveted Best Paper and Best Student Paper selections.
Earlier this week the Association for Computational Linguistics (ACL) 2018 announced its Best Two Short Papers, neither of which had yet been published. Today the AI community got its first look at one of the winners when Know What You Don’t Know: Unanswerable Questions for SQuAD was released on arXiv.
At this year’s ATEC Developer Challenge, Futuristic won Best Creative Idea Award, while RADIAnt won Best Social Value Award.
Chinese e-commerce giant Alibaba announced on September 11 that the respected University of Michigan professor will lead fundamental research on quantum technology at its Seattle lab.
Andrew Ng, one of the top minds in deep learning, loves teaching. And so, the Stanford University professor decided toContinue Reading