Honglak Lee

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Google ∙ University of Michigan

Research Scientist at Google Brain, Associate Professor of CSE University of Michigan

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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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Neural Kinematic Networks for Unsupervised Motion Retargetting

We propose a recurrent neural network architecture with a Forward Kinema...
04/16/2018 ∙ by Ruben Villegas, et al. ∙ 0 ∙ share

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Unsupervised Discovery of Object Landmarks as Structural Representations

Deep neural networks can model images with rich latent representations, ...
04/12/2018 ∙ by Yuting Zhang, et al. ∙ 0 ∙ share

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Hierarchical Novelty Detection for Visual Object Recognition

Deep neural networks have achieved impressive success in large-scale vis...
04/02/2018 ∙ by Kibok Lee, et al. ∙ 1 ∙ share

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An efficient framework for learning sentence representations

In this work we propose a simple and efficient framework for learning se...
03/07/2018 ∙ by Lajanugen Logeswaran, et al. ∙ 0 ∙ share

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Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis

We propose a novel hierarchical approach for text-to-image synthesis by ...
01/16/2018 ∙ by Seunghoon Hong, et al. ∙ 0 ∙ share

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Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples

The problem of detecting whether a test sample is from in-distribution (...
11/26/2017 ∙ by Kimin Lee, et al. ∙ 1 ∙ share

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Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs

In this paper, we study the problem of addressee and response selection ...
09/12/2017 ∙ by Rui Zhang, et al. ∙ 0 ∙ share

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Learning 6-DOF Grasping Interaction with Deep Geometry-aware 3D Representations

This paper focuses on the problem of learning 6-DOF grasping with a para...
08/24/2017 ∙ by Xinchen Yan, et al. ∙ 0 ∙ share

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Value Prediction Network

This paper proposes a novel deep reinforcement learning (RL) architectur...
07/11/2017 ∙ by Junhyuk Oh, et al. ∙ 0 ∙ share

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Decomposing Motion and Content for Natural Video Sequence Prediction

We propose a deep neural network for the prediction of future frames in ...
06/25/2017 ∙ by Ruben Villegas, et al. ∙ 0 ∙ share

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