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.
Researchers from the University of Sheffield, Beihang University, and Open University’s Knowledge Media Institute have proposed a transfer learning approach that can automatically process historical texts at a semantic level to generate modern language summaries.
UmlsBERT is a deep Transformer network architecture that incorporates clinical domain knowledge from a clinical Metathesaurus in order to build ‘semantically enriched’ contextual representations that will benefit from both the contextual learning and domain knowledge.
This paper is the first to systematically study the security of Multi-Sensor Fusion (MSF) based localization in high-autonomy Autonomous Vehicles (AVs). Researchers design FusionRipper, a novel and general attack that opportunistically captures and exploits take-over vulnerabilities.