Honglak Lee

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Google ∙ University of Michigan

Senior Vice President and Chief Scientist of LG AI Research; Associate Professor of CSE University of Michigan

Featured Co-authors

Sergey Levine
249 publications
Bo Li
224 publications
Pieter Abbeel
170 publications
Rui Zhang
163 publications
Yi Zhang
122 publications
Ming-Hsuan Yang
115 publications
Bernt Schiele
107 publications
Quoc V. Le
90 publications
Pushmeet Kohli
88 publications
Abhinav Gupta
76 publications
Jinwoo Shin
62 publications

Revisiting Hierarchical Approach for Persistent Long-Term Video Prediction

Learning to predict the long-term future of video frames is notoriously ...
04/14/2021 ∙ by Wonkwang Lee, et al. ∙ 1 ∙ share

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Adversarial Environment Generation for Learning to Navigate the Web

Learning to autonomously navigate the web is a difficult sequential deci...
03/02/2021 ∙ by Izzeddin Gür, et al. ∙ 14 ∙ share

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State Entropy Maximization with Random Encoders for Efficient Exploration

Recent exploration methods have proven to be a recipe for improving samp...
02/18/2021 ∙ by Younggyo Seo, et al. ∙ 13 ∙ share

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

The output of text-to-image synthesis systems should be coherent, clear,...
01/12/2021 ∙ by Han Zhang, et al. ∙ 16 ∙ share

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Evolving Reinforcement Learning Algorithms

We propose a method for meta-learning reinforcement learning algorithms ...
01/08/2021 ∙ by John D. Co-Reyes, et al. ∙ 28 ∙ share

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Few-shot Sequence Learning with Transformers

Few-shot algorithms aim at learning new tasks provided only a handful of...
12/17/2020 ∙ by Lajanugen Logeswaran, et al. ∙ 9 ∙ share

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

Localized Narratives is a dataset with detailed natural language descrip...
11/07/2020 ∙ by Jing Yu Koh, et al. ∙ 5 ∙ share

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What's in a Loss Function for Image Classification?

It is common to use the softmax cross-entropy loss to train neural netwo...
10/30/2020 ∙ by Simon Kornblith, et al. ∙ 31 ∙ share

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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...
10/28/2020 ∙ by Wilka Carvalho, et al. ∙ 10 ∙ share

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Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

Sample efficiency has been one of the major challenges for deep reinforc...
10/23/2020 ∙ by Guangxiang Zhu, et al. ∙ 5 ∙ share

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i-Mix: A Strategy for Regularizing Contrastive Representation Learning

Contrastive representation learning has shown to be an effective way of ...
10/17/2020 ∙ by Kibok Lee, et al. ∙ 4 ∙ share

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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...
08/11/2020 ∙ by Tianhao Zhang, et al. ∙ 7 ∙ share

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Predictive Information Accelerates Learning in RL

The Predictive Information is the mutual information between the past an...
07/24/2020 ∙ by Kuang-Huei Lee, et al. ∙ 21 ∙ share

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An Ode to an ODE

We present a new paradigm for Neural ODE algorithms, calledODEtoODE, whe...
06/19/2020 ∙ by Krzysztof Choromanski, et al. ∙ 18 ∙ share

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CompressNet: Generative Compression at Extremely Low Bitrates

Compressing images at extremely low bitrates (< 0.1 bpp) has always been...
06/14/2020 ∙ by Suraj Kiran Raman, et al. ∙ 11 ∙ share

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

Model-based reinforcement learning (RL) enjoys several benefits, such as...
05/14/2020 ∙ by Kimin Lee, et al. ∙ 7 ∙ share

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Time Dependence in Non-Autonomous Neural ODEs

Neural Ordinary Differential Equations (ODEs) are elegant reinterpretati...
05/05/2020 ∙ by Jared Quincy Davis, et al. ∙ 7 ∙ share

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Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Netw...
02/11/2020 ∙ by Zhengli Zhao, et al. ∙ 38 ∙ share

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BRPO: Batch Residual Policy Optimization

In batch reinforcement learning (RL), one often constrains a learned pol...
02/08/2020 ∙ by Sungryull Sohn, et al. ∙ 11 ∙ share

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High-Fidelity Synthesis with Disentangled Representation

Learning disentangled representation of data without supervision is an i...
01/13/2020 ∙ by Wonkwang Lee, et al. ∙ 0 ∙ share

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Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies

We propose and address a novel few-shot RL problem, where a task is char...
01/01/2020 ∙ by Sungryull Sohn, et al. ∙ 15 ∙ share

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Efficient Adversarial Training with Transferable Adversarial Examples

Adversarial training is an effective defense method to protect classific...
12/27/2019 ∙ by Haizhong Zheng, et al. ∙ 12 ∙ share

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How Should an Agent Practice?

We present a method for learning intrinsic reward functions to drive the...
12/15/2019 ∙ by Janarthanan Rajendran, et al. ∙ 17 ∙ share

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High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks

Predicting future video frames is extremely challenging, as there are ma...
11/05/2019 ∙ by Ruben Villegas, et al. ∙ 37 ∙ share

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Consistency Regularization for Generative Adversarial Networks

Generative Adversarial Networks (GANs) are known to be difficult to trai...
10/26/2019 ∙ by Han Zhang, et al. ∙ 28 ∙ share

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A Simple Randomization Technique for Generalization in Deep Reinforcement Learning

Deep reinforcement learning (RL) agents often fail to generalize to unse...
10/11/2019 ∙ by Kimin Lee, et al. ∙ 24 ∙ share

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IEG: Robust Neural Network Training to Tackle Severe Label Noise

Collecting large-scale data with clean labels for supervised training of...
10/01/2019 ∙ by Zizhao Zhang, et al. ∙ 10 ∙ share

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

Hierarchical reinforcement learning has demonstrated significant success...
09/23/2019 ∙ by Ofir Nachum, et al. ∙ 14 ∙ share

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

This paper proposes a method for learning a trajectory-conditioned polic...
07/24/2019 ∙ by Yijie Guo, et al. ∙ 1 ∙ share

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Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networks

Training a deep network policy for robot manipulation is notoriously cos...
06/21/2019 ∙ by Xinchen Yan, et al. ∙ 3 ∙ share

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SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing

Deep neural networks (DNNs) have achieved great success in various appli...
06/19/2019 ∙ by Haonan Qiu, et al. ∙ 7 ∙ share

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Unsupervised Learning of Object Structure and Dynamics from Videos

Extracting and predicting object structure and dynamics from videos with...
06/19/2019 ∙ by Matthias Minderer, et al. ∙ 4 ∙ share

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Zero-Shot Entity Linking by Reading Entity Descriptions

We present the zero-shot entity linking task, where mentions must be lin...
06/18/2019 ∙ by Lajanugen Logeswaran, et al. ∙ 0 ∙ share

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Similarity of Neural Network Representations Revisited

Recent work has sought to understand the behavior of neural networks by ...
05/01/2019 ∙ by Simon Kornblith, et al. ∙ 20 ∙ share

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Incremental Learning with Unlabeled Data in the Wild

Deep neural networks are known to suffer from catastrophic forgetting in...
03/29/2019 ∙ by Kibok Lee, et al. ∙ 13 ∙ share

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Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incor...
01/31/2019 ∙ by Kimin Lee, et al. ∙ 7 ∙ share

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Diversity-Sensitive Conditional Generative Adversarial Networks

We propose a simple yet highly effective method that addresses the mode-...
01/25/2019 ∙ by Dingdong Yang, et al. ∙ 16 ∙ share

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Generative Adversarial Self-Imitation Learning

This paper explores a simple regularizer for reinforcement learning by p...
12/03/2018 ∙ by Yijie Guo, et al. ∙ 0 ∙ share

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Learning Latent Dynamics for Planning from Pixels

Planning has been very successful for control tasks with known environme...
11/12/2018 ∙ by Danijar Hafner, et al. ∙ 6 ∙ share

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Contingency-Aware Exploration in Reinforcement Learning

This paper investigates whether learning contingency-awareness and contr...
11/05/2018 ∙ by Jongwook Choi, et al. ∙ 8 ∙ share

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Content preserving text generation with attribute controls

In this work, we address the problem of modifying textual attributes of ...
11/03/2018 ∙ by Lajanugen Logeswaran, et al. ∙ 0 ∙ share

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Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

We study the problem of representation learning in goal-conditioned hier...
10/02/2018 ∙ by Ofir Nachum, et al. ∙ 12 ∙ share

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Learning Hierarchical Semantic Image Manipulation through Structured Representations

Understanding, reasoning, and manipulating semantic concepts of images h...
08/22/2018 ∙ by Seunghoon Hong, et al. ∙ 2 ∙ share

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MT-VAE: Learning Motion Transformations to Generate Multimodal Human Dynamics

Long-term human motion can be represented as a series of motion modes---...
08/14/2018 ∙ by Xinchen Yan, et al. ∙ 2 ∙ share

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Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies

We introduce a new RL problem where the agent is required to execute a g...
07/19/2018 ∙ by Sungryull Sohn, et al. ∙ 0 ∙ share

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A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

Detecting test samples drawn sufficiently far away from the training dis...
07/10/2018 ∙ by Kimin Lee, et al. ∙ 3 ∙ share

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Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

Integrating model-free and model-based approaches in reinforcement learn...
07/04/2018 ∙ by Jacob Buckman, et al. ∙ 6 ∙ share

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Self-Imitation Learning

This paper proposes Self-Imitation Learning (SIL), a simple off-policy a...
06/14/2018 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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Hierarchical Long-term Video Prediction without Supervision

Much of recent research has been devoted to video prediction and generat...
06/12/2018 ∙ by Nevan Wichers, et al. ∙ 10 ∙ share

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Data-Efficient Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) is a promising approach to ext...
05/21/2018 ∙ by Ofir Nachum, et al. ∙ 0 ∙ share

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