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Multi-Rate VAE: Train Once, Get the Full Rate-Distortion Curve
Juhan Bae, Michael R. Zhang, Michael Ruan +4
Variational autoencoders (VAEs) are powerful tools for learning latent representations of data used in a wide range of applications. In practice, VAEs usually require multiple trai…
Large Language Models Are Human-Level Prompt Engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han +4
By conditioning on natural language instructions, large language models (LLMs) have displayed impressive capabilities as general-purpose computers. However, task performance depend…
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…
Dataset Distillation using Neural Feature Regression
Yongchao Zhou, Ehsan Nezhadarya, Jimmy Ba
Dataset distillation aims to learn a small synthetic dataset that preserves most of the information from the original dataset. Dataset distillation can be formulated as a bi-level…
You Can't Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments
Keiran Paster, Sheila McIlraith, Jimmy Ba
Recently, methods such as Decision Transformer that reduce reinforcement learning to a prediction task and solve it via supervised learning (RvS) have become popular due to their s…