Differentiable Tree Operations Promote Compositional Generalization

1 code implementation • 1 Jun 2023 • Paul Soulos, Edward Hu, Kate McCurdy, Yunmo Chen, Roland Fernandez, Paul Smolensky, Jianfeng Gao

To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree operations into subsymbolic matrix operations on tensors.

Semantic Parsing Text Generation

GFlowNets and variational inference

1 code implementation • 2 Oct 2022 • Nikolay Malkin, Salem Lahlou, Tristan Deleu, Xu Ji, Edward Hu, Katie Everett, Dinghuai Zhang, Yoshua Bengio

This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs.

Reinforcement Learning (RL) Variational Inference

Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

1 code implementation • NeurIPS 2021 • Ge Yang, Edward Hu, Igor Babuschkin, Szymon Sidor, Xiaodong Liu, David Farhi, Nick Ryder, Jakub Pachocki, Weizhu Chen, Jianfeng Gao

Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovered Maximal Update Parametrization ($\mu$P), many optimal HPs remain stable even as model size changes.

Composing Complex Skills by Learning Transition Policies with Proximity Reward Induction

no code implementations • ICLR 2019 • Youngwoon Lee*, Shao-Hua Sun*, Sriram Somasundaram, Edward Hu, Joseph J. Lim

Intelligent creatures acquire complex skills by exploiting previously learned skills and learning to transition between them.

Continuous Control