471 citations · 1.3k across the 24 of their papers we have counts for
24 papers · 1 filter
AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback
Yann Dubois, Xuechen Li, Rohan Taori +6
Large language models (LLMs) such as ChatGPT have seen widespread adoption due to their strong instruction-following abilities. Developing these LLMs involves a complex yet poorly…
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
Zhaoyan Liu, Noel Vouitsis, Satya Krishna Gorti +2
We propose TR0N, a highly general framework to turn pre-trained unconditional generative models, such as GANs and VAEs, into conditional models. The conditioning can be highly arbi…
Exploring Low Rank Training of Deep Neural Networks
Siddhartha Rao Kamalakara, Acyr Locatelli, Bharat Venkitesh +3
Training deep neural networks in low rank, i.e. with factorised layers, is of particular interest to the community: it offers efficiency over unfactorised training in terms of both…
Learning Domain Invariant Representations in Goal-conditioned Block MDPs
Beining Han, Chongyi Zheng, Harris Chan +3
Deep Reinforcement Learning (RL) is successful in solving many complex Markov Decision Processes (MDPs) problems. However, agents often face unanticipated environmental changes aft…
Planning from Pixels using Inverse Dynamics Models
Keiran Paster, Sheila A. McIlraith, Jimmy Ba
Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by l…
A Study of Gradient Variance in Deep Learning
Fartash Faghri, David Duvenaud, David J. Fleet +1
The impact of gradient noise on training deep models is widely acknowledged but not well understood. In this context, we study the distribution of gradients during training. We int…