BAIR blog

website   |   @berkeley_ai   |  

The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, and robotics. BAIR includes over two dozen faculty and more than a hundred graduate students pursuing research on fundamental advances in the above areas as well as cross-cutting themes including multi-modal deep learning, human-compatible AI, and connecting AI with other scientific disciplines and the humanities. The BAIR Blog provides an accessible, general-audience medium for BAIR researchers to communicate research findings, perspectives on the field, and various updates. Posts are written by students, post-docs, and faculty in BAIR, and are intended to provide relevant and timely discussion of research findings and results, both to experts and the general audience.

recent posts:

›   Learning what to do by simulating the past

›   The importance of hyperparameter optimization for model-based reinforcement learning

›   Pretrained transformers as universal computation engines

›   Maximum entropy RL (provably) solves some robust RL problems

›   Self-supervised policy adaptation during deployment

›   The successor representation, gamma-models, and infinite-horizon prediction

›   Does GPT-2 know your phone number?

›   Offline reinforcement learning: how conservative algorithms can enable new applications

›   Learning state abstractions for long-horizon planning

›   EvolveGraph: dynamic neural relational reasoning for interacting systems

›   Training on test inputs with amortized conditional normalized maximum likelihood

›   Goodhart’s law, diversity and a series of seemingly unrelated toy problems

›   Adapting on the fly to test time distribution shift

›   Reinforcement learning is supervised learning on optimized data

›   Plan2Explore: active model-building for self-supervised visual reinforcement learning

›   AWAC: accelerating online reinforcement learning with offline datasets

›   AI will change the world. Who will change AI? We will.

›   Exploring exploration: comparing children with RL agents in unified environments

›   Can RL from pixels be as efficient as RL from state?

›   Decentralized reinforcement learning: global decision-making via local economic transactions

›   D4RL: building better benchmarks for offline reinforcement learning

›   Open compound domain adaptation

›   OmniTact: a multi-directional high-resolution touch sensor

›   The ingredients of real world robotic reinforcement learning

›   Making decision trees accurate again: explaining what explainable AI did not

›   Robots learning to move like animals

›   Physically realistic attacks on deep reinforcement learning

›   Unsupervised meta-learning: learning to learn without supervision

›   Does on-policy data collection fix errors in off-policy reinforcement learning?

›   BADGR: the Berkeley autonomous driving ground robot

next page →

©2021 - Association for the Understanding of Artificial Intelligence

©2021 - Association for the Understanding of Artificial Intelligence-