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Found 5 papers, 1 papers with code
Statistical discrimination in learning agents
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.
Challenges in Detoxifying Language Models
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks.
Large-Scale Visual Speech Recognition
To achieve this, we constructed the largest existing visual speech recognition dataset, consisting of pairs of text and video clips of faces speaking (3, 886 hours of video).
Ranked #14 on
Lipreading
on LRS3-TED
(using extra training data)
Deep Reinforcement Learning in Large Discrete Action Spaces
Being able to reason in an environment with a large number of discrete actions is essential to bringing reinforcement learning to a larger class of problems.
Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions
Further, we use deep deterministic policy gradients to learn a policy that for each position of the slate, guides attention towards the part of the action space in which the value is the highest and we only evaluate actions in this area.
