Search Results for author: Jingwei Zhang

Found 22 papers, 4 papers with code

Boosting Deep Neural Network Efficiency with Dual-Module Inference

no code implementations ICML 2020 Liu Liu, Lei Deng, Zhaodong Chen, yuke wang, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie

Using Deep Neural Networks (DNNs) in machine learning tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements and energy constraints because of the memory-bound and the compute-bound execution pattern of DNNs.

Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention

no code implementations9 Jul 2021 Jingwei Zhang, Bin Zi, Xiaoyu Ge

This paper seeks to tackle the bin packing problem (BPP) through a learning perspective.

reinforcement-learning

Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory

no code implementations22 Aug 2020 Jialun Liu, Jingwei Zhang, Yi Yang, Wenhui Li, Chi Zhang, Yifan Sun

With slight modifications, MBJ is applicable for two fundamental visual recognition tasks, \emph{i. e.}, deep image classification and deep metric learning (on long-tailed data).

Data Augmentation General Classification +3

CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions

no code implementations ECCV 2020 Zhongdao Wang, Jingwei Zhang, Liang Zheng, Yixuan Liu, Yifan Sun, Ya-Li Li, Shengjin Wang

This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.

Multi-Object Tracking Person Re-Identification +1

Efficiency and Equity are Both Essential: A Generalized Traffic Signal Controller with Deep Reinforcement Learning

no code implementations9 Mar 2020 Shengchao Yan, Jingwei Zhang, Daniel Büscher, Wolfram Burgard

In this paper we present an approach to learning policies for signal controllers using deep reinforcement learning aiming for optimized traffic flow.

reinforcement-learning

Generalization Bounds for Convolutional Neural Networks

no code implementations3 Oct 2019 Shan Lin, Jingwei Zhang

Convolutional neural networks (CNNs) have achieved breakthrough performances in a wide range of applications including image classification, semantic segmentation, and object detection.

Generalization Bounds Image Classification +2

Dual-module Inference for Efficient Recurrent Neural Networks

no code implementations25 Sep 2019 Liu Liu, Lei Deng, Shuangchen Li, Jingwei Zhang, Yihua Yang, Zhenyu Gu, Yufei Ding, Yuan Xie

Using Recurrent Neural Networks (RNNs) in sequence modeling tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements because of the memory-bound execution pattern of RNNs.

Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration

no code implementations18 Mar 2019 Jingwei Zhang, Niklas Wetzel, Nicolai Dorka, Joschka Boedecker, Wolfram Burgard

Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration.

An Optimal Transport View on Generalization

no code implementations8 Nov 2018 Jingwei Zhang, Tongliang Liu, DaCheng Tao

We derive upper bounds on the generalization error of learning algorithms based on their \emph{algorithmic transport cost}: the expected Wasserstein distance between the output hypothesis and the output hypothesis conditioned on an input example.

Learning Theory

An Information-Theoretic View for Deep Learning

no code implementations24 Apr 2018 Jingwei Zhang, Tongliang Liu, DaCheng Tao

This upper bound shows that as the number of convolutional and pooling layers $L$ increases in the network, the expected generalization error will decrease exponentially to zero.

Speech Recognition

On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier

no code implementations11 Feb 2018 Jingwei Zhang, Tongliang Liu, DaCheng Tao

We study the rates of convergence from empirical surrogate risk minimizers to the Bayes optimal classifier.

VR-Goggles for Robots: Real-to-sim Domain Adaptation for Visual Control

no code implementations1 Feb 2018 Jingwei Zhang, Lei Tai, Peng Yun, Yufeng Xiong, Ming Liu, Joschka Boedecker, Wolfram Burgard

In this paper, we deal with the reality gap from a novel perspective, targeting transferring Deep Reinforcement Learning (DRL) policies learned in simulated environments to the real-world domain for visual control tasks.

Domain Adaptation Style Transfer

Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning

1 code implementation6 Oct 2017 Lei Tai, Jingwei Zhang, Ming Liu, Wolfram Burgard

Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning.

Autonomous Vehicles Imitation Learning

Neural SLAM: Learning to Explore with External Memory

1 code implementation29 Jun 2017 Jingwei Zhang, Lei Tai, Ming Liu, Joschka Boedecker, Wolfram Burgard

We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments.

reinforcement-learning Simultaneous Localization and Mapping

A Survey of Deep Network Solutions for Learning Control in Robotics: From Reinforcement to Imitation

1 code implementation21 Dec 2016 Lei Tai, Jingwei Zhang, Ming Liu, Joschka Boedecker, Wolfram Burgard

We carry out our discussions on the two main paradigms for learning control with deep networks: deep reinforcement learning and imitation learning.

Imitation Learning reinforcement-learning

Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments

no code implementations16 Dec 2016 Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, Wolfram Burgard

We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances.

Frame reinforcement-learning +1

Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections

1 code implementation EMNLP 2015 Jingwei Zhang, Aaron Gerow, Jaan Altosaar, James Evans, Richard Jean So

Weak topic correlation across document collections with different numbers of topics in individual collections presents challenges for existing cross-collection topic models.

Topic Models

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