55 citations · 78 across the 7 of their papers we have counts for
7 papers · 1 filter
On the benefits of pixel-based hierarchical policies for task generalization
Tudor Cristea-Platon, Bogdan Mazoure, Josh Susskind +1
Reinforcement learning practitioners often avoid hierarchical policies, especially in image-based observation spaces. Typically, the single-task performance improvement over flat-p…
Efficient Non-Parametric Uncertainty Quantification for Black-Box Large Language Models and Decision Planning
Yao-Hung Hubert Tsai, Walter Talbott, Jian Zhang
Step-by-step decision planning with large language models (LLMs) is gaining attention in AI agent development. This paper focuses on decision planning with uncertainty estimation t…
Value function estimation using conditional diffusion models for control
Bogdan Mazoure, Walter Talbott, Miguel Angel Bautista +3
A fairly reliable trend in deep reinforcement learning is that the performance scales with the number of parameters, provided a complimentary scaling in amount of training data. As…
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation
David Berthelot, Arnaud Autef, Jierui Lin +6
Denoising Diffusion models have demonstrated their proficiency for generative sampling. However, generating good samples often requires many iterations. Consequently, techniques su…
Position Prediction as an Effective Pretraining Strategy
Shuangfei Zhai, Navdeep Jaitly, Jason Ramapuram +7
Transformers have gained increasing popularity in a wide range of applications, including Natural Language Processing (NLP), Computer Vision and Speech Recognition, because of thei…
Efficient Representation Learning via Adaptive Context Pooling
Chen Huang, Walter Talbott, Navdeep Jaitly +1
Self-attention mechanisms model long-range context by using pairwise attention between all input tokens. In doing so, they assume a fixed attention granularity defined by the indiv…