580 citations · 1.3k across the 31 of their papers we have counts for
7 papers
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks
Yijie Guo, Qiucheng Wu, Honglak Lee
Meta reinforcement learning (meta-RL) aims to learn a policy solving a set of training tasks simultaneously and quickly adapting to new tasks. It requires massive amounts of data d…
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min +4
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat…
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents
Rui Zhang, Honglak Lee, Dragomir Radev
The goal of sentence and document modeling is to accurately represent the meaning of sentences and documents for various Natural Language Processing tasks. In this work, we present…
Learning What and Where to Draw
Scott Reed, Zeynep Akata, Santosh Mohan +3
Generative Adversarial Networks (GANs) have recently demonstrated the capability to synthesize compelling real-world images, such as room interiors, album covers, manga, faces, bir…
Deep Variational Canonical Correlation Analysis
Weiran Wang, Xinchen Yan, Honglak Lee +1
We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA to nonlinear…
Training Deep Neural Networks on Noisy Labels with Bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov +3
Current state-of-the-art deep learning systems for visual object recognition and detection use purely supervised training with regularization such as dropout to avoid overfitting.…