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Found 6 papers, 1 papers with code
Regularized Behavior Value Estimation
Due to bootstrapping, these errors get amplified during training and can lead to divergence, thereby crippling learning.
Addressing Extrapolation Error in Deep Offline Reinforcement Learning
These errors can be compounded by bootstrapping when the function approximator overestimates, leading the value function to *grow unbounded*, thereby crippling learning.
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning
We hope that our suite of benchmarks will increase the reproducibility of experiments and make it possible to study challenging tasks with a limited computational budget, thus making RL research both more systematic and more accessible across the community.
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 #11 on
Lipreading
on LRS3-TED
(using extra training data)
Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions
Deep autoregressive models have shown state-of-the-art performance in density estimation for natural images on large-scale datasets such as ImageNet.
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training
The ability to train large-scale neural networks has resulted in state-of-the-art performance in many areas of computer vision.
