b'Honglak Lee'

research

∙ 07/19/2022

Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks

Meta reinforcement learning (meta-RL) aims to learn a policy solving a s...

Yijie Guo, et al. ∙

research

∙ 07/06/2022

Pure Transformers are Powerful Graph Learners

We show that standard Transformers without graph-specific modifications ...

Jinwoo Kim, et al. ∙

research

∙ 06/16/2022

OpenSRH: optimizing brain tumor surgery using intraoperative stimulated Raman histology

Accurate intraoperative diagnosis is essential for providing safe and ef...

Cheng Jiang, et al. ∙

research

∙ 06/16/2022

Is Continual Learning Truly Learning Representations Continually?

Continual learning (CL) aims to learn from sequentially arriving tasks w...

Sungmin Cha, et al. ∙

research

∙ 05/28/2022

Few-shot Subgoal Planning with Language Models

Pre-trained large language models have shown successful progress in many...

Lajanugen Logeswaran, et al. ∙

research

∙ 05/25/2022

LEPUS: Prompt-based Unsupervised Multi-hop Reranking for Open-domain QA

We study unsupervised multi-hop reranking for multi-hop QA (MQA) with op...

Muhammad Khalifa, et al. ∙

research

∙ 05/25/2022

Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization

We tackle real-world problems with complex structures beyond the pixel-b...

Sungryull Sohn, et al. ∙

research

∙ 05/14/2022

RiCS: A 2D Self-Occlusion Map for Harmonizing Volumetric Objects

There have been remarkable successes in computer vision with deep learni...

Yunseok Jang, et al. ∙

research

∙ 04/06/2022

Simple and Effective Synthesis of Indoor 3D Scenes

We study the problem of synthesizing immersive 3D indoor scenes from one...

Jing Yu Koh, et al. ∙

research

∙ 03/28/2022

Learning Parameterized Task Structure for Generalization to Unseen Entities

Real world tasks are hierarchical and compositional. Tasks can be compos...

Anthony Z. Liu, et al. ∙

research

∙ 03/18/2022

SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning

Preference-based reinforcement learning (RL) has shown potential for tea...

Jongjin Park, et al. ∙

research

∙ 03/15/2022

Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution

Recent vision transformers along with self-attention have achieved promi...

Jinsu Yoo, et al. ∙

research

∙ 02/02/2022

Lipschitz-constrained Unsupervised Skill Discovery

We study the problem of unsupervised skill discovery, whose goal is to l...

Seohong Park, et al. ∙

research

∙ 01/21/2022

Environment Generation for Zero-Shot Compositional Reinforcement Learning

Many real-world problems are compositional - solving them requires compl...

Izzeddin Gür, et al. ∙

research

∙ 11/22/2021

L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transfo...

Taehoon Kim, et al. ∙

research

∙ 11/18/2021

Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning

Operating in the real-world often requires agents to learn about a compl...

Christopher Hoang, et al. ∙

research

∙ 11/18/2021

Improving Transferability of Representations via Augmentation-Aware Self-Supervision

Recent unsupervised representation learning methods have shown to be eff...

Hankook Lee, et al. ∙

research

∙ 08/08/2021

Contrastive Representation Learning for Rapid Intraoperative Diagnosis of Skull Base Tumors Imaged Using Stimulated Raman Histology

Background: Accurate diagnosis of skull base tumors is essential for pro...

Cheng Jiang, et al. ∙

research

∙ 07/13/2021

Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks

We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on ...

Sungryull Sohn, et al. ∙

research

∙ 06/02/2021

Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning

Learning to reach goal states and learning diverse skills through mutual...

Jongwook Choi, et al. ∙

research

∙ 05/18/2021

Pathdreamer: A World Model for Indoor Navigation

People navigating in unfamiliar buildings take advantage of myriad visua...

Jing Yu Koh, et al. ∙

research

∙ 04/14/2021

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

Learning to predict the long-term future of video frames is notoriously ...

Wonkwang Lee, et al. ∙

research

∙ 03/02/2021

Adversarial Environment Generation for Learning to Navigate the Web

Learning to autonomously navigate the web is a difficult sequential deci...

Izzeddin Gür, et al. ∙

research

∙ 02/18/2021

State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving samp...

Younggyo Seo, et al. ∙

research

∙ 01/12/2021

Cross-Modal Contrastive Learning for Text-to-Image Generation

The output of text-to-image synthesis systems should be coherent, clear,...

Han Zhang, et al. ∙

research

∙ 01/08/2021

Evolving Reinforcement Learning Algorithms

We propose a method for meta-learning reinforcement learning algorithms ...

John D. Co-Reyes, et al. ∙

research

∙ 12/17/2020

Few-shot Sequence Learning with Transformers

Few-shot algorithms aim at learning new tasks provided only a handful of...

Lajanugen Logeswaran, et al. ∙

research

∙ 11/07/2020

Text-to-Image Generation Grounded by Fine-Grained User Attention

Localized Narratives is a dataset with detailed natural language descrip...

Jing Yu Koh, et al. ∙

research

∙ 10/30/2020

What's in a Loss Function for Image Classification?

It is common to use the softmax cross-entropy loss to train neural netwo...

Simon Kornblith, et al. ∙

research

∙ 10/28/2020

Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in First-person Simulated 3D Environments

First-person object-interaction tasks in high-fidelity, 3D, simulated en...

Wilka Carvalho, et al. ∙

research

∙ 10/23/2020

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Sample efficiency has been one of the major challenges for deep reinforc...

Guangxiang Zhu, et al. ∙

research

∙ 10/17/2020

i-Mix: A Strategy for Regularizing Contrastive Representation Learning

Contrastive representation learning has shown to be an effective way of ...

Kibok Lee, et al. ∙

research

∙ 08/11/2020

Text as Neural Operator: Image Manipulation by Text Instruction

In this paper, we study a new task that allows users to edit an input im...

Tianhao Zhang, et al. ∙

research

∙ 07/24/2020

Predictive Information Accelerates Learning in RL

The Predictive Information is the mutual information between the past an...

Kuang-Huei Lee, et al. ∙

research

∙ 06/19/2020

An Ode to an ODE

We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...

Krzysztof Choromanski, et al. ∙

research

∙ 06/14/2020

CompressNet: Generative Compression at Extremely Low Bitrates

Compressing images at extremely low bitrates (< 0.1 bpp) has always been...

Suraj Kiran Raman, et al. ∙

research

∙ 05/14/2020

Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning

Model-based reinforcement learning (RL) enjoys several benefits, such as...

Kimin Lee, et al. ∙

research

∙ 05/05/2020

Time Dependence in Non-Autonomous Neural ODEs

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretati...

Jared Quincy Davis, et al. ∙

research

∙ 02/11/2020

Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Netw...

Zhengli Zhao, et al. ∙

research

∙ 02/08/2020

BRPO: Batch Residual Policy Optimization

In batch reinforcement learning (RL), one often constrains a learned pol...

Sungryull Sohn, et al. ∙

research

∙ 01/13/2020

High-Fidelity Synthesis with Disentangled Representation

Learning disentangled representation of data without supervision is an i...

Wonkwang Lee, et al. ∙

research

∙ 01/01/2020

Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

We propose and address a novel few-shot RL problem, where a task is char...

Sungryull Sohn, et al. ∙

research

∙ 12/27/2019

Efficient Adversarial Training with Transferable Adversarial Examples

Adversarial training is an effective defense method to protect classific...

Haizhong Zheng, et al. ∙

research

∙ 12/15/2019

How Should an Agent Practice?

We present a method for learning intrinsic reward functions to drive the...

Janarthanan Rajendran, et al. ∙

research

∙ 11/05/2019

High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Predicting future video frames is extremely challenging, as there are ma...

Ruben Villegas, et al. ∙

research

∙ 10/26/2019

Consistency Regularization for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are known to be difficult to trai...

Han Zhang, et al. ∙

research

∙ 10/11/2019

A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...

Kimin Lee, et al. ∙

research

∙ 10/01/2019

IEG: Robust Neural Network Training to Tackle Severe Label Noise

Collecting large-scale data with clean labels for supervised training of...

Zizhao Zhang, et al. ∙

research

∙ 09/23/2019

Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?

Hierarchical reinforcement learning has demonstrated significant success...

Ofir Nachum, et al. ∙

research

∙ 07/24/2019

Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy

This paper proposes a method for learning a trajectory-conditioned polic...

Yijie Guo, et al. ∙

Honglak Lee

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