Search Results for author: Yi Wu

Found 51 papers, 20 papers with code

Sequence Level Contrastive Learning for Text Summarization

no code implementations8 Sep 2021 Shusheng Xu, Xingxing Zhang, Yi Wu, Furu Wei

In this paper, we propose a contrastive learning model for supervised abstractive text summarization, where we view a document, its gold summary and its model generated summaries as different views of the same mean representation and maximize the similarities between them during training.

Abstractive Text Summarization Contrastive Learning +2

Temporal Induced Self-Play for Stochastic Bayesian Games

1 code implementation21 Aug 2021 Weizhe Chen, Zihan Zhou, Yi Wu, Fei Fang

One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward.

Learning to Design and Construct Bridge without Blueprint

no code implementations5 Aug 2021 Yunfei Li, Tao Kong, Lei LI, Yifeng Li, Yi Wu

In this task, the robot needs to first design a feasible bridge architecture for arbitrarily wide cliffs and then manipulate the blocks reliably to construct a stable bridge according to the proposed design.

Curriculum Learning Motion Planning

Disentangled Attention as Intrinsic Regularization for Bimanual Multi-Object Manipulation

no code implementations10 Jun 2021 Minghao Zhang, Pingcheng Jian, Yi Wu, Huazhe Xu, Xiaolong Wang

To tackle these two issues, we propose a novel technique called disentangled attention, which provides an intrinsic regularization for two robots to focus on separate sub-tasks and objects.

FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia

no code implementations26 Apr 2021 Longling Zhang, Bochen Shen, Ahmed Barnawi, Shan Xi, Neeraj Kumar, Yi Wu

Under the FL framework and Differentially Private thinking, we propose a FederatedDifferentially Private Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for sustainable smart cities.

Federated Learning

Classifying herbal medicine origins by temporal and spectral data mining of electronic nose

1 code implementation14 Apr 2021 Li Liu, Xianghao Zhan, Ziheng Duan, Yi Wu, Rumeng Wu, Xiaoqing Guan, Zhan Wang, You Wang, Guang Li

In this study, we classified different origins of three categories of herbal medicines with different feature extraction methods: manual feature extraction, mathematical transformation, deep learning algorithms.

Dimensionality Reduction

Theoretically Improving Graph Neural Networks via Anonymous Walk Graph Kernels

no code implementations7 Apr 2021 Qingqing Long, Yilun Jin, Yi Wu, Guojie Song

However, the inability of GNNs to model substructures in graphs remains a significant drawback.

Graph Mining

Reference-Aided Part-Aligned Feature Disentangling for Video Person Re-Identification

no code implementations21 Mar 2021 Guoqing Zhang, Yuhao Chen, Yang Dai, yuhui Zheng, Yi Wu

Due to the inaccurate person detections and pose changes, pedestrian misalignment significantly increases the difficulty of feature extraction and matching.

Video-Based Person Re-Identification

Solving Compositional Reinforcement Learning Problems via Task Reduction

1 code implementation ICLR 2021 Yunfei Li, Yilin Wu, Huazhe Xu, Xiaolong Wang, Yi Wu

We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems.

Continuous Control

Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

2 code implementations ICLR 2021 Zhenggang Tang, Chao Yu, Boyuan Chen, Huazhe Xu, Xiaolong Wang, Fei Fang, Simon Du, Yu Wang, Yi Wu

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games.

The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games

1 code implementation2 Mar 2021 Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu

Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings.

Starcraft Starcraft II

Growth, Electronic Structure and Superconductivity of Ultrathin Epitaxial CoSi2 Films

no code implementations21 Jan 2021 Yuan Fang, Ding Wang, Peng Li, Hang Su, Tian Le, Yi Wu, Guo-Wei Yang, Hua-Li Zhang, Zhi-Guang Xiao, Yan-Qiu Sun, Si-Yuan Hong, Yan-Wu Xie, Huan-Hua Wang, Chao Cao, Xin Lu, Hui-Qiu Yuan, Yang Liu

We report growth, electronic structure and superconductivity of ultrathin epitaxial CoSi2 films on Si(111).

Mesoscale and Nanoscale Physics

Deep Q-Learning with Low Switching Cost

no code implementations1 Jan 2021 Shusheng Xu, Simon Shaolei Du, Yi Wu

We initiate the study on deep reinforcement learning problems that require low switching cost, i. e., small number of policy switches during training.

Atari Games Q-Learning +2

Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms

no code implementations1 Jan 2021 Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu

We benchmark commonly used multi-agent deep reinforcement learning (MARL) algorithms on a variety of cooperative multi-agent games.

Starcraft

BeBold: Exploration Beyond the Boundary of Explored Regions

2 code implementations15 Dec 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.

Curriculum Learning Efficient Exploration +1

Charge density wave and weak Kondo effect in a Dirac semimetal CeSbTe

no code implementations23 Nov 2020 Peng Li, Baijiang Lv, Yuan Fang, Wei Guo, Zhongzheng Wu, Yi Wu, Cheng-Maw Cheng, Dawei Shen, Yuefeng Nie, Luca Petaccia, Chao Cao, Zhu-An Xu, Yang Liu

Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe.

Strongly Correlated Electrons Materials Science

Multi-Agent Collaboration via Reward Attribution Decomposition

1 code implementation16 Oct 2020 Tianjun Zhang, Huazhe Xu, Xiaolong Wang, Yi Wu, Kurt Keutzer, Joseph E. Gonzalez, Yuandong Tian

In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.

Dota 2 Multi-agent Reinforcement Learning +2

Streaming Graph Neural Networks via Continual Learning

no code implementations23 Sep 2020 Junshan Wang, Guojie Song, Yi Wu, Liang Wang

In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step.

Continual Learning Node Classification

Multi-Task Reinforcement Learning with Soft Modularization

1 code implementation NeurIPS 2020 Ruihan Yang, Huazhe Xu, Yi Wu, Xiaolong Wang

While training multiple tasks jointly allow the policies to share parameters across different tasks, the optimization problem becomes non-trivial: It remains unclear what parameters in the network should be reused across tasks, and how the gradients from different tasks may interfere with each other.

Meta-Learning Multi-Task Learning

Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning

1 code implementation ICLR 2020 Qian Long, Zihan Zhou, Abhibav Gupta, Fei Fang, Yi Wu, Xiaolong Wang

In multi-agent games, the complexity of the environment can grow exponentially as the number of agents increases, so it is particularly challenging to learn good policies when the agent population is large.

Curriculum Learning Multi-agent Reinforcement Learning

Influence-Based Multi-Agent Exploration

1 code implementation ICLR 2020 Tonghan Wang, Jianhao Wang, Yi Wu, Chongjie Zhang

We present two exploration methods: exploration via information-theoretic influence (EITI) and exploration via decision-theoretic influence (EDTI), by exploiting the role of interaction in coordinated behaviors of agents.

PPGAN: Privacy-preserving Generative Adversarial Network

no code implementations4 Oct 2019 Yi Liu, Jialiang Peng, James J. Q. Yu, Yi Wu

To address this issue, we propose a Privacy-preserving Generative Adversarial Network (PPGAN) model, in which we achieve differential privacy in GANs by adding well-designed noise to the gradient during the model learning procedure.

Emergent Tool Use From Multi-Agent Autocurricula

3 code implementations ICLR 2020 Bowen Baker, Ingmar Kanitscheider, Todor Markov, Yi Wu, Glenn Powell, Bob McGrew, Igor Mordatch

Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination.

Bayesian Relational Memory for Semantic Visual Navigation

1 code implementation ICCV 2019 Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

We introduce a new memory architecture, Bayesian Relational Memory (BRM), to improve the generalization ability for semantic visual navigation agents in unseen environments, where an agent is given a semantic target to navigate towards.

Visual Navigation

Fairness in Recommendation Ranking through Pairwise Comparisons

no code implementations2 Mar 2019 Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.

Fairness Recommendation Systems

Deep Reinforcement Learning for Green Security Games with Real-Time Information

no code implementations6 Nov 2018 Yufei Wang, Zheyuan Ryan Shi, Lantao Yu, Yi Wu, Rohit Singh, Lucas Joppa, Fei Fang

Green Security Games (GSGs) have been proposed and applied to optimize patrols conducted by law enforcement agencies in green security domains such as combating poaching, illegal logging and overfishing.

Q-Learning

Learning and Planning with a Semantic Model

no code implementations ICLR 2019 Yi Wu, Yuxin Wu, Aviv Tamar, Stuart Russell, Georgia Gkioxari, Yuandong Tian

Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.

Visual Navigation

Robust Fuzzy-Learning For Partially Overlapping Channels Allocation In UAV Communication Networks

no code implementations28 Jun 2018 Chaoqiong Fan, Bin Li, Jia Hou, Yi Wu, Weisi Guo, Chenglin Zhao

This allows the system to achieve a smoother and more robust performance by optimizing in an alternate space.

Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms

no code implementations ICML 2018 Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell

Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements.

Probabilistic Programming

Near-Linear Time Local Polynomial Nonparametric Estimation with Box Kernels

no code implementations26 Feb 2018 Yining Wang, Yi Wu, Simon S. Du

Local polynomial regression (Fan and Gijbels 1996) is an important class of methods for nonparametric density estimation and regression problems.

Density Estimation

Building Generalizable Agents with a Realistic and Rich 3D Environment

5 code implementations ICLR 2018 Yi Wu, Yuxin Wu, Georgia Gkioxari, Yuandong Tian

To generalize to unseen environments, an agent needs to be robust to low-level variations (e. g. color, texture, object changes), and also high-level variations (e. g. layout changes of the environment).

Data Augmentation

Adversarial Training for Relation Extraction

no code implementations EMNLP 2017 Yi Wu, David Bamman, Stuart Russell

Adversarial training is a mean of regularizing classification algorithms by generating adversarial noise to the training data.

General Classification Image Classification +3

Meta-Learning MCMC Proposals

no code implementations NeurIPS 2018 Tongzhou Wang, Yi Wu, David A. Moore, Stuart J. Russell

The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required.

Meta-Learning Named Entity Recognition

CoupleNet: Coupling Global Structure with Local Parts for Object Detection

3 code implementations ICCV 2017 Yousong Zhu, Chaoyang Zhao, Jinqiao Wang, Xu Zhao, Yi Wu, Hanqing Lu

To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection.

Object Detection Region Proposal

Improving the Annotation of Sentence Specificity

no code implementations LREC 2016 Junyi Jessy Li, Bridget O{'}Daniel, Yi Wu, Wenli Zhao, Ani Nenkova

We found that the lack of specificity distributes evenly among immediate prior context, long distance prior context and no prior context.

Towards Practical Bayesian Parameter and State Estimation

no code implementations29 Mar 2016 Yusuf Bugra Erol, Yi Wu, Lei LI, Stuart Russell

Joint state and parameter estimation is a core problem for dynamic Bayesian networks.

Value Iteration Networks

8 code implementations NeurIPS 2016 Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, Pieter Abbeel

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within.

Adaptive Compressive Tracking via Online Vector Boosting Feature Selection

no code implementations21 Apr 2015 Qingshan Liu, Jing Yang, Kaihua Zhang, Yi Wu

Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features.

Feature Selection Rectification

Tractability and Decompositions of Global Cost Functions

no code implementations9 Feb 2015 David Allouche, Christian Bessiere, Patrice Boizumault, Simon de Givry, Patricia Gutierrez, Jimmy H. M. Lee, Kam Lun Leung, Samir Loudni, Jean-Philippe Métivier, Thomas Schiex, Yi Wu

A global cost function is called tractable projection-safe when applying an EPT to it is tractable and does not break the tractability property.

Robust Visual Tracking via Convolutional Networks

no code implementations19 Jan 2015 Kaihua Zhang, Qingshan Liu, Yi Wu, Ming-Hsuan Yang

In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking.

Visual Tracking

Online Object Tracking: A Benchmark

no code implementations CVPR 2013 Yi Wu, Jongwoo Lim, Ming-Hsuan Yang

Object tracking is one of the most important components in numerous applications of computer vision.

Object Tracking

Dual-Space Analysis of the Sparse Linear Model

no code implementations NeurIPS 2012 Yi Wu, David P. Wipf

In contrast, for analyses of update rules and sparsity properties of local and global solutions, as well as extensions to more general likelihood models, we can leverage coefficient-space techniques developed for Type I and apply them to Type II.

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