Search Results for author: Jun Wang

Found 521 papers, 167 papers with code

Parametric Local Metric Learning for Nearest Neighbor Classification

no code implementations NeurIPS 2012 Jun Wang, Alexandros Kalousis, Adam Woznica

We present a new parametric local metric learning method in which we learn a smooth metric matrix function over the data manifold.

Classification General Classification +1

Question Answering Against Very-Large Text Collections

no code implementations26 Apr 2013 Leon Derczynski, Richard Shaw, Ben Solway, Jun Wang

Question answering involves developing methods to extract useful information from large collections of documents.

Information Retrieval Question Answering +1

Two-Stage Metric Learning

no code implementations12 May 2014 Jun Wang, Ke Sun, Fei Sha, Stephane Marchand-Maillet, Alexandros Kalousis

This induces in the input data space a new family of distance metric with unique properties.

Metric Learning Vocal Bursts Valence Prediction

A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising

no code implementations20 May 2014 Bo-Wei Chen, Shuai Yuan, Jun Wang

From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB.

Computer Science and Game Theory

Real-Time Bidding Benchmarking with iPinYou Dataset

2 code implementations25 Jul 2014 Wei-Nan Zhang, Shuai Yuan, Jun Wang, Xuehua Shen

This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation.

Computer Science and Game Theory Computers and Society

A Survey on Soft Subspace Clustering

no code implementations19 Sep 2014 Zhaohong Deng, Kup-Sze Choi, Yizhang Jiang, Jun Wang, Shitong Wang

Subspace clustering (SC) is a promising clustering technology to identify clusters based on their associations with subspaces in high dimensional spaces.

Clustering

Exploiting Feature and Class Relationships in Video Categorization with Regularized Deep Neural Networks

no code implementations25 Feb 2015 Yu-Gang Jiang, Zuxuan Wu, Jun Wang, xiangyang xue, Shih-Fu Chang

In this paper, we study the challenging problem of categorizing videos according to high-level semantics such as the existence of a particular human action or a complex event.

Deep Attributes from Context-Aware Regional Neural Codes

no code implementations8 Sep 2015 Jianwei Luo, Jianguo Li, Jun Wang, Zhiguo Jiang, Yurong Chen

Results show that deep attribute approaches achieve state-of-the-art results, and outperforms existing peer methods with a significant margin, even though some benchmarks have little overlap of concepts with the pre-trained CNN models.

Attribute General Classification +2

Learning to Hash for Indexing Big Data - A Survey

no code implementations17 Sep 2015 Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang

Such learning to hash methods exploit information such as data distributions or class labels when optimizing the hash codes or functions.

Factorizing LambdaMART for cold start recommendations

no code implementations4 Nov 2015 Phong Nguyen, Jun Wang, Alexandros Kalousis

Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees.

Learning-To-Rank Matrix Completion +1

Space-Time Local Embeddings

no code implementations NeurIPS 2015 Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet

We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space.

Dimensionality Reduction

Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR Estimation

no code implementations11 Jan 2016 Wei-Nan Zhang, Lingxi Chen, Jun Wang

In this work, we propose a general framework which learns the user profiles based on their online browsing behaviour, and transfers the learned knowledge onto prediction of their ad response.

Collaborative Filtering Transfer Learning

Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction

5 code implementations11 Jan 2016 Wei-Nan Zhang, Tianming Du, Jun Wang

Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known.

Click-Through Rate Prediction

Feedback Control of Real-Time Display Advertising

1 code implementation3 Mar 2016 Wei-Nan Zhang, Yifei Rong, Jun Wang, Tianchi Zhu, Xiaofan Wang

In this paper, we propose a feedback control mechanism for RTB which helps advertisers dynamically adjust the bids to effectively control the KPIs, e. g., the auction winning ratio and the effective cost per click.

Computer Science and Game Theory Systems and Control

Feature Selection as a Multiagent Coordination Problem

no code implementations16 Mar 2016 Kleanthis Malialis, Jun Wang, Gary Brooks, George Frangou

In this paper, we formulate feature selection as a multiagent coordination problem and propose a novel feature selection method using multiagent reinforcement learning.

feature selection reinforcement-learning +1

Online Reconstruction of Indoor Scenes From RGB-D Streams

no code implementations CVPR 2016 Hao Wang, Jun Wang, Wang Liang

A system capable of performing robust online volumetric reconstruction of indoor scenes based on input from a handheld RGB-D camera is presented.

Learning text representation using recurrent convolutional neural network with highway layers

no code implementations22 Jun 2016 Ying Wen, Wei-Nan Zhang, Rui Luo, Jun Wang

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks.

Sentiment Analysis

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

23 code implementations18 Sep 2016 Lantao Yu, Wei-Nan Zhang, Jun Wang, Yong Yu

As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data.

Reinforcement Learning (RL) Text Generation

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

1 code implementation7 Oct 2016 Jun Wang, Wei-Nan Zhang, Shuai Yuan

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads.

Computer Science and Game Theory

Product-based Neural Networks for User Response Prediction

11 code implementations1 Nov 2016 Yanru Qu, Han Cai, Kan Ren, Wei-Nan Zhang, Yong Yu, Ying Wen, Jun Wang

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.

Click-Through Rate Prediction Recommendation Systems

Character-level Convolutional Network for Text Classification Applied to Chinese Corpus

1 code implementation14 Nov 2016 Wei-Jie Huang, Jun Wang

This article provides an interesting exploration of character-level convolutional neural network solving Chinese corpus text classification problem.

General Classification text-classification +1

Real-Time Bidding by Reinforcement Learning in Display Advertising

1 code implementation10 Jan 2017 Han Cai, Kan Ren, Wei-Nan Zhang, Kleanthis Malialis, Jun Wang, Yong Yu, Defeng Guo

In this paper, we formulate the bid decision process as a reinforcement learning problem, where the state space is represented by the auction information and the campaign's real-time parameters, while an action is the bid price to set.

reinforcement-learning Reinforcement Learning (RL)

Effective scaling registration approach by imposing the emphasis on the scale factor

no code implementations28 Apr 2017 Minmin Xu, Siyu Xu, Jihua Zhu, Yaochen Li, Jun Wang, Huimin Lu

This paper proposes an effective approach for the scaling registration of $m$-D point sets.

IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models

3 code implementations30 May 2017 Jun Wang, Lantao Yu, Wei-Nan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, Dell Zhang

This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval focusing on predicting relevant documents given a query, and the discriminative retrieval focusing on predicting relevancy given a query-document pair.

Document Ranking Information Retrieval +2

Learning to Design Games: Strategic Environments in Reinforcement Learning

no code implementations5 Jul 2017 Haifeng Zhang, Jun Wang, Zhiming Zhou, Wei-Nan Zhang, Ying Wen, Yong Yu, Wenxin Li

In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment.

reinforcement-learning Reinforcement Learning (RL)

Efficient Architecture Search by Network Transformation

3 code implementations16 Jul 2017 Han Cai, Tianyao Chen, Wei-Nan Zhang, Yong Yu, Jun Wang

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results.

Image Classification Neural Architecture Search +2

Inception Score, Label Smoothing, Gradient Vanishing and -log(D(x)) Alternative

no code implementations5 Aug 2017 Zhiming Zhou, Wei-Nan Zhang, Jun Wang

In this article, we mathematically study several GAN related topics, including Inception score, label smoothing, gradient vanishing and the -log(D(x)) alternative.

Unsupervised Generative Modeling Using Matrix Product States

1 code implementation6 Sep 2017 Zhao-Yu Han, Jun Wang, Heng Fan, Lei Wang, Pan Zhang

Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence.

BIG-bench Machine Learning

A Study of AI Population Dynamics with Million-agent Reinforcement Learning

no code implementations13 Sep 2017 Yaodong Yang, Lantao Yu, Yiwei Bai, Jun Wang, Wei-Nan Zhang, Ying Wen, Yong Yu

We conduct an empirical study on discovering the ordered collective dynamics obtained by a population of intelligence agents, driven by million-agent reinforcement learning.

reinforcement-learning Reinforcement Learning (RL)

Long Text Generation via Adversarial Training with Leaked Information

6 code implementations24 Sep 2017 Jiaxian Guo, Sidi Lu, Han Cai, Wei-Nan Zhang, Yong Yu, Jun Wang

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc.

Sentence Text Generation

Multi-view Registration Based on Weighted Low Rank and Sparse Matrix Decomposition of Motions

no code implementations25 Sep 2017 Congcong Jin, Jihua Zhu, Yaochen Li, Shanmin Pang, Lei Chen, Jun Wang

Then, it proposes the weighted LRS decomposition, where each block element is assigned with one estimated weight to denote its reliability.

Set-to-Set Hashing with Applications in Visual Recognition

no code implementations2 Nov 2017 I-Hong Jhuo, Jun Wang

In this paper, we consider the fundamental problem of finding a nearest set from a collection of sets, to a query set.

Retrieval

Happiness Pursuit: Personality Learning in a Society of Agents

no code implementations29 Nov 2017 Rafał Muszyński, Jun Wang

We find that the agents that achieve higher happiness during testing against hand-coded AI, have lower happiness when competing against each other.

Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning

1 code implementation NeurIPS 2018 Rui Luo, Jianhong Wang, Yaodong Yang, Zhanxing Zhu, Jun Wang

We propose a new sampling method, the thermostat-assisted continuously-tempered Hamiltonian Monte Carlo, for Bayesian learning on large datasets and multimodal distributions.

A Neural Stochastic Volatility Model

no code implementations30 Nov 2017 Rui Luo, Wei-Nan Zhang, Xiaojun Xu, Jun Wang

In this paper, we show that the recent integration of statistical models with deep recurrent neural networks provides a new way of formulating volatility (the degree of variation of time series) models that have been widely used in time series analysis and prediction in finance.

Time Series Time Series Analysis

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

3 code implementations2 Dec 2017 Lianmin Zheng, Jiacheng Yang, Han Cai, Wei-Nan Zhang, Jun Wang, Yong Yu

Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.

Multi-agent Reinforcement Learning reinforcement-learning +1

Scalable Quantum Tomography with Fidelity Estimation

1 code implementation8 Dec 2017 Jun Wang, Zhao-Yu Han, Song-Bo Wang, Zeyang Li, Liang-Zhu Mu, Heng Fan, Lei Wang

We propose a quantum tomography scheme for pure qudit systems which adopts random base measurements and generative learning methods, along with a built-in fidelity estimation approach to assess the reliability of the tomographic states.

Quantum Physics

Learning Continuous User Representations through Hybrid Filtering with doc2vec

no code implementations31 Dec 2017 Simon Stiebellehner, Jun Wang, Shuai Yuan

In order to maximize the predictive performance of our look-alike modeling algorithms, we propose two novel hybrid filtering techniques that utilize the recent neural probabilistic language model algorithm doc2vec.

Language Modelling

tau-FPL: Tolerance-Constrained Learning in Linear Time

no code implementations15 Jan 2018 Ao Zhang, Nan Li, Jian Pu, Jun Wang, Junchi Yan, Hongyuan Zha

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications.

Texygen: A Benchmarking Platform for Text Generation Models

1 code implementation6 Feb 2018 Yaoming Zhu, Sidi Lu, Lei Zheng, Jiaxian Guo, Wei-Nan Zhang, Jun Wang, Yong Yu

We introduce Texygen, a benchmarking platform to support research on open-domain text generation models.

Benchmarking Text Generation

Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising

no code implementations1 Mar 2018 Kan Ren, Wei-Nan Zhang, Ke Chang, Yifei Rong, Yong Yu, Jun Wang

From the learning perspective, we show that the bidding machine can be updated smoothly with both offline periodical batch or online sequential training schemes.

BIG-bench Machine Learning

Exponential Discriminative Metric Embedding in Deep Learning

no code implementations7 Mar 2018 Bowen Wu, Zhangling Chen, Jun Wang, Huaming Wu

With the remarkable success achieved by the Convolutional Neural Networks (CNNs) in object recognition recently, deep learning is being widely used in the computer vision community.

Face Recognition Face Verification +3

Neural Text Generation: Past, Present and Beyond

no code implementations15 Mar 2018 Sidi Lu, Yaoming Zhu, Wei-Nan Zhang, Jun Wang, Yong Yu

This paper presents a systematic survey on recent development of neural text generation models.

Benchmarking reinforcement-learning +2

Detecting Adversarial Examples via Key-based Network

no code implementations2 Jun 2018 Pinlong Zhao, Zhouyu Fu, Ou wu, QinGhua Hu, Jun Wang

In contrast to existing defense methods, the proposed method does not require knowledge of the process for generating adversarial examples and can be applied to defend against different types of attacks.

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

1 code implementation11 Aug 2018 Kan Ren, Yuchen Fang, Wei-Nan Zhang, Shuhao Liu, Jiajun Li, Ya zhang, Yong Yu, Jun Wang

To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation.

Learning Adaptive Display Exposure for Real-Time Advertising

no code implementations10 Sep 2018 Weixun Wang, Junqi Jin, Jianye Hao, Chunjie Chen, Chuan Yu, Wei-Nan Zhang, Jun Wang, Xiaotian Hao, Yixi Wang, Han Li, Jian Xu, Kun Gai

In this paper, we investigate the problem of advertising with adaptive exposure: can we dynamically determine the number and positions of ads for each user visit under certain business constraints so that the platform revenue can be increased?

Factorized Q-Learning for Large-Scale Multi-Agent Systems

no code implementations11 Sep 2018 Yong Chen, Ming Zhou, Ying Wen, Yaodong Yang, Yufeng Su, Wei-Nan Zhang, Dell Zhang, Jun Wang, Han Liu

Deep Q-learning has achieved a significant success in single-agent decision making tasks.

Multiagent Systems

Learning to Communicate Implicitly By Actions

no code implementations10 Oct 2018 Zheng Tian, Shihao Zou, Ian Davies, Tim Warr, Lisheng Wu, Haitham Bou Ammar, Jun Wang

The auxiliary reward for communication is integrated into the learning of the policy module.

Learning Shared Dynamics with Meta-World Models

no code implementations5 Nov 2018 Lisheng Wu, Minne Li, Jun Wang

Humans have consciousness as the ability to perceive events and objects: a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions.

Atari Games Multi-Task Learning

Matrix Recovery with Implicitly Low-Rank Data

1 code implementation9 Nov 2018 Xingyu Xie, Jianlong Wu, Guangcan Liu, Jun Wang

To tackle this issue, we propose a novel method for matrix recovery in this paper, which could well handle the case where the target matrix is low-rank in an implicit feature space but high-rank or even full-rank in its original form.

Layout Design for Intelligent Warehouse by Evolution with Fitness Approximation

no code implementations14 Nov 2018 Haifeng Zhang, Zilong Guo, Han Cai, Chris Wang, Wei-Nan Zhang, Yong Yu, Wenxin Li, Jun Wang

With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume.

Layout Design

Pelee: A Real-Time Object Detection System on Mobile Devices

2 code implementations NeurIPS 2018 Jun Wang, Tanner Bohn, Charles Ling

In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.

object-detection Real-Time Object Detection

Transfer Representation Learning with TSK Fuzzy System

no code implementations9 Jan 2019 Peng Xu, Zhaohong Deng, Jun Wang, Qun Zhang, Shitong Wang

A core issue in transfer learning is to learn a shared feature space in where the distributions of the data from two domains are matched.

Dimensionality Reduction Representation Learning +1

Predicting the Mumble of Wireless Channel with Sequence-to-Sequence Models

no code implementations14 Jan 2019 Yourui Huangfu, Jian Wang, Rong Li, Chen Xu, Xianbin Wang, Huazi Zhang, Jun Wang

Accurate prediction of fading channel in future is essential to realize adaptive transmission and other methods that can save power and provide gains.

Language Modelling Machine Translation +4

Attention-aware Multi-stroke Style Transfer

1 code implementation CVPR 2019 Yuan Yao, Jianqiang Ren, Xuansong Xie, Weidong Liu, Yong-Jin Liu, Jun Wang

Neural style transfer has drawn considerable attention from both academic and industrial field.

Style Transfer

Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning

no code implementations26 Jan 2019 Ying Wen, Yaodong Yang, Rui Luo, Jun Wang

Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem.

Decision Making Multi-agent Reinforcement Learning

Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning

no code implementations ICLR 2019 Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan

Our methods are tested on both the matrix game and the differential game, which have a non-trivial equilibrium where common gradient-based methods fail to converge.

Multi-agent Reinforcement Learning reinforcement-learning +1

Feature Concatenation Multi-view Subspace Clustering

1 code implementation30 Jan 2019 Qinghai Zheng, Jihua Zhu, Zhongyu Li, Shanmin Pang, Jun Wang, Yaochen Li

To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.

Clustering Multi-view Subspace Clustering

End-to-end feature fusion siamese network for adaptive visual tracking

no code implementations4 Feb 2019 Dongyan Guo, Jun Wang, Weixuan Zhao, Ying Cui, Zhenhua Wang, Sheng-Yong Chen

Both features and the channel weights are utilized in a template generation layer to generate a discriminative template.

Visual Tracking

Learning to Flip Successive Cancellation Decoding of Polar Codes with LSTM Networks

no code implementations22 Feb 2019 Xianbin Wang, Huazi Zhang, Rong Li, Lingchen Huang, Shengchen Dai, Yourui Huangfu, Jun Wang

Specifically, before each SC decoding attempt, a long short-term memory (LSTM) network is exploited to either (i) locate the first error bit, or (ii) undo a previous `wrong' flip.

Joint Perception and Control as Inference with an Object-based Implementation

no code implementations4 Mar 2019 Minne Li, Zheng Tian, Pranav Nashikkar, Ian Davies, Ying Wen, Jun Wang

Existing model-based reinforcement learning methods often study perception modeling and decision making separately.

Bayesian Inference Decision Making +2

Reinforcement Learning for Nested Polar Code Construction

no code implementations16 Apr 2019 Lingchen Huang, Huazi Zhang, Rong Li, Yiqun Ge, Jun Wang

In this paper, we model nested polar code construction as a Markov decision process (MDP), and tackle it with advanced reinforcement learning (RL) techniques.

reinforcement-learning Reinforcement Learning (RL)

Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

no code implementations24 Apr 2019 Peng Xu, Zhaohong Deng, Chen Cui, Te Zhang, Kup-Sze Choi, Gu Suhang, Jun Wang, Shitong Wang

Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weakens the clarity and interpretability of TSK FS.

Clustering Sparse Learning

Multiple Independent Subspace Clusterings

no code implementations10 May 2019 Xing Wang, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guo-Qiang Xiao, Maozu Guo

To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering.

Clustering

Ranking-based Deep Cross-modal Hashing

no code implementations11 May 2019 Xuanwu Liu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Yazhou Ren, Maozu Guo

Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions.

Cross-Modal Retrieval Retrieval

Graph Attention Memory for Visual Navigation

no code implementations11 May 2019 Dong Li, Qichao Zhang, Dongbin Zhao, Yuzheng Zhuang, Bin Wang, Wulong Liu, Rasul Tutunov, Jun Wang

To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.

Graph Attention Reinforcement Learning (RL) +1

Multi-View Multi-Instance Multi-Label Learning based on Collaborative Matrix Factorization

no code implementations13 May 2019 Yuying Xing, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Zili Zhang, Maozu Guo

To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices.

Multi-Label Learning

Multi-View Multiple Clustering

no code implementations13 May 2019 Shixing Yao, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

It then uses matrix factorization on the individual matrices, along with the shared matrix, to generate diverse clusterings of high-quality.

Clustering Representation Learning

BayesNAS: A Bayesian Approach for Neural Architecture Search

no code implementations13 May 2019 Hongpeng Zhou, Minghao Yang, Jun Wang, Wei Pan

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training.

Neural Architecture Search

ActiveHNE: Active Heterogeneous Network Embedding

no code implementations14 May 2019 Xia Chen, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Zhao Li, Xiangliang Zhang

To maximize the profit of utilizing the rare and valuable supervised information in HNEs, we develop a novel Active Heterogeneous Network Embedding (ActiveHNE) framework, which includes two components: Discriminative Heterogeneous Network Embedding (DHNE) and Active Query in Heterogeneous Networks (AQHN).

Network Embedding

Deep Reinforcement Learning for Scheduling in Cellular Networks

no code implementations15 May 2019 Jian Wang, Chen Xu, Yourui Huangfu, Rong Li, Yiqun Ge, Jun Wang

Integrating artificial intelligence (AI) into wireless networks has drawn significant interest in both industry and academia.

reinforcement-learning Reinforcement Learning (RL) +1

Learning discriminative features in sequence training without requiring framewise labelled data

no code implementations16 May 2019 Jun Wang, Dan Su, Jie Chen, Shulin Feng, Dongpeng Ma, Na Li, Dong Yu

We propose a novel method which simultaneously models both the sequence discriminative training and the feature discriminative learning within a single network architecture, so that it can learn discriminative deep features in sequence training that obviates the need for presegmented training data.

A Regularized Opponent Model with Maximum Entropy Objective

1 code implementation17 May 2019 Zheng Tian, Ying Wen, Zhichen Gong, Faiz Punakkath, Shihao Zou, Jun Wang

In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality".

Multi-agent Reinforcement Learning reinforcement-learning +1

Joint Information Preservation for Heterogeneous Domain Adaptation

no code implementations22 May 2019 Peng Xu, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang

The two domains often lie in different feature spaces due to diverse data collection methods, which leads to the more challenging task of heterogeneous domain adaptation (HDA).

Domain Adaptation

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

no code implementations27 May 2019 Jiarui Jin, Ming Zhou, Wei-Nan Zhang, Minne Li, Zilong Guo, Zhiwei Qin, Yan Jiao, Xiaocheng Tang, Chenxi Wang, Jun Wang, Guobin Wu, Jieping Ye

How to optimally dispatch orders to vehicles and how to trade off between immediate and future returns are fundamental questions for a typical ride-hailing platform.

Multiagent Systems

Weakly-paired Cross-Modal Hashing

no code implementations29 May 2019 Xuanwu Liu, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

FlexCMH first introduces a clustering-based matching strategy to explore the local structure of each cluster, and thus to find the potential correspondence between clusters (and samples therein) across modalities.

Clustering Retrieval

Replica-exchange Nosé-Hoover dynamics for Bayesian learning on large datasets

no code implementations29 May 2019 Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.

General Classification Image Classification

Speech-based Estimation of Bulbar Regression in Amyotrophic Lateral Sclerosis

no code implementations WS 2019 Alan Wisler, Kristin Teplansky, Jordan Green, Yana Yunusova, Thomas Campbell, Daragh Heitzman, Jun Wang

Experimental results demonstrated the AFSFRS-R bulbar subscore can be predicted from speech samples, which has clinical implication for automatic monitoring of the disease progression of ALS using speech information.

regression

Neural Variational Inference For Estimating Uncertainty in Knowledge Graph Embeddings

1 code implementation12 Jun 2019 Alexander I. Cowen-Rivers, Pasquale Minervini, Tim Rocktaschel, Matko Bosnjak, Sebastian Riedel, Jun Wang

Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data.

Knowledge Graph Embeddings Knowledge Graphs +2

NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations

1 code implementation17 Jun 2019 Qiaoyun Wu, Dinesh Manocha, Jun Wang, Kai Xu

First, the latent distribution is conditioned on current observations and the target view, leading to a model-based, target-driven navigation.

Visual Navigation

Realistic Channel Models Pre-training

no code implementations22 Jul 2019 Yourui Huangfu, Jian Wang, Chen Xu, Rong Li, Yiqun Ge, Xianbin Wang, Huazi Zhang, Jun Wang

In this paper, we propose a neural-network-based realistic channel model with both the similar accuracy as deterministic channel models and uniformity as stochastic channel models.

Wasserstein Robust Reinforcement Learning

no code implementations30 Jul 2019 Mohammed Amin Abdullah, Hang Ren, Haitham Bou Ammar, Vladimir Milenkovic, Rui Luo, Mingtian Zhang, Jun Wang

Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world.

reinforcement-learning Reinforcement Learning (RL)

Cross-modal Zero-shot Hashing

no code implementations19 Aug 2019 Xuanwu Liu, Zhao Li, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

It then defines an objective function to achieve deep feature learning compatible with the composite similarity preserving, category attribute space learning, and hashing coding function learning.

Attribute Retrieval

DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks

1 code implementation27 Aug 2019 Sen Deng, Mingqiang Wei, Jun Wang, Luming Liang, Haoran Xie, Meng Wang

We have validated our approach on four recognized datasets (three synthetic and one real-world).

Rain Removal

MANAS: Multi-Agent Neural Architecture Search

no code implementations3 Sep 2019 Vasco Lopes, Fabio Maria Carlucci, Pedro M Esperança, Marco Singh, Victor Gabillon, Antoine Yang, Hang Xu, Zewei Chen, Jun Wang

The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective.

Neural Architecture Search

Bi-level Actor-Critic for Multi-agent Coordination

1 code implementation8 Sep 2019 Haifeng Zhang, Weizhe Chen, Zeren Huang, Minne Li, Yaodong Yang, Wei-Nan Zhang, Jun Wang

Coordination is one of the essential problems in multi-agent systems.

Multiagent Systems

MarlRank: Multi-agent Reinforced Learning to Rank

no code implementations15 Sep 2019 Shihao Zou, Zhonghua Li, Mohammad Akbari, Jun Wang, Peng Zhang

By defining reward as a function of NDCG, we can optimize our model directly on the ranking performance measure.

Document Ranking Learning-To-Rank

Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee

no code implementations25 Sep 2019 Minghao Han, Yuan Tian, Lixian Zhang, Jun Wang, Wei Pan

Reinforcement learning (RL) offers a principled way to achieve the optimal cumulative performance index in discrete-time nonlinear stochastic systems, which are modeled as Markov decision processes.

Continuous Control Open-Ended Question Answering +1

Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching

no code implementations25 Sep 2019 Peng Zhang, Xiaoliu Mao, Xindian Ma, Benyou Wang, Jing Zhang, Jun Wang, Dawei Song

We prove that by a mapping (via the trace operator) on the high-dimensional matching matrix, a low-dimensional attention matrix can be derived.

Inductive Bias Question Answering +2

Variational Constrained Reinforcement Learning with Application to Planning at Roundabout

no code implementations25 Sep 2019 Yuan Tian, Minghao Han, Lixian Zhang, Wulong Liu, Jun Wang, Wei Pan

In this paper, we combine variational learning and constrained reinforcement learning to simultaneously learn a Conditional Representation Model (CRM) to encode the states into safe and unsafe distributions respectively as well as to learn the corresponding safe policy.

Autonomous Driving reinforcement-learning +1

GREASE: A Generative Model for Relevance Search over Knowledge Graphs

no code implementations11 Oct 2019 Tianshuo Zhou, Ziyang Li, Gong Cheng, Jun Wang, Yu'Ang Wei

The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities.

Knowledge Graphs

Multi-View Reinforcement Learning

1 code implementation NeurIPS 2019 Minne Li, Lisheng Wu, Haitham Bou Ammar, Jun Wang

This paper is concerned with multi-view reinforcement learning (MVRL), which allows for decision making when agents share common dynamics but adhere to different observation models.

Decision Making reinforcement-learning +1

Mixup-breakdown: a consistency training method for improving generalization of speech separation models

no code implementations28 Oct 2019 Max W. Y. Lam, Jun Wang, Dan Su, Dong Yu

Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditions.

Speech Separation

Detecting Causal Language Use in Science Findings

no code implementations IJCNLP 2019 Bei Yu, Yingya Li, Jun Wang

We then applied the prediction model to measure the causal language use in the research conclusions of about 38, 000 observational studies in PubMed.

Misinformation

Active Multi-Label Crowd Consensus

no code implementations7 Nov 2019 Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi, Jun Wang, Xiangliang Zhang

AMCC accounts for the commonality and individuality of workers, and assumes that workers can be organized into different groups.

$H_\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee

1 code implementation7 Nov 2019 Minghao Han, Yuan Tian, Lixian Zhang, Jun Wang, Wei Pan

In this paper, we introduce and extend the idea of robust stability and $H_\infty$ control to design policies with both stability and robustness guarantee.

Autonomous Driving reinforcement-learning +2

Buffer-aware Wireless Scheduling based on Deep Reinforcement Learning

no code implementations13 Nov 2019 Chen Xu, Jian Wang, Tianhang Yu, Chuili Kong, Yourui Huangfu, Rong Li, Yiqun Ge, Jun Wang

In this paper, the downlink packet scheduling problem for cellular networks is modeled, which jointly optimizes throughput, fairness and packet drop rate.

Fairness reinforcement-learning +2

SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking

2 code implementations CVPR 2020 Dongyan Guo, Jun Wang, Ying Cui, Zhenhua Wang, Sheng-Yong Chen

The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction.

Classification General Classification +3

Multi-View Multiple Clusterings using Deep Matrix Factorization

no code implementations26 Nov 2019 Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta, Xiangliang Zhang

Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results.

Clustering

Spectrum Cartography via Coupled Block-Term Tensor Decomposition

no code implementations28 Nov 2019 Guoyong Zhang, Xiao Fu, Jun Wang, Xi-Le Zhao, Mingyi Hong

Spectrum cartography aims at estimating power propagation patterns over a geographical region across multiple frequency bands (i. e., a radio map)---from limited samples taken sparsely over the region.

Spectrum Cartography Tensor Decomposition

Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning

no code implementations3 Dec 2019 Hangyu Mao, Wulong Liu, Jianye Hao, Jun Luo, Dong Li, Zhengchao Zhang, Jun Wang, Zhen Xiao

Social psychology and real experiences show that cognitive consistency plays an important role to keep human society in order: if people have a more consistent cognition about their environments, they are more likely to achieve better cooperation.

Multi-agent Reinforcement Learning Q-Learning +2

Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization

1 code implementation9 Dec 2019 Qiaoyun Wu, Kai Xu, Jun Wang, Mingliang Xu, Dinesh Manocha

The regularization maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions.

Robotics

Attention-Aware Answers of the Crowd

no code implementations24 Dec 2019 Jingzheng Tu, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

However, they all assume that workers' label quality is stable over time (always at the same level whenever they conduct the tasks).

Bayesian Inference

Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach

no code implementations27 Dec 2019 Jun Wang, Hefu Zhang, Qi Liu, Zhen Pan, Hanqing Tao

However, few of them take into account the inherent decision-making process between investors and crowdfunding dynamics.

Continuous Control Decision Making +4

Multi-Agent Interactions Modeling with Correlated Policies

1 code implementation ICLR 2020 Minghuan Liu, Ming Zhou, Wei-Nan Zhang, Yuzheng Zhuang, Jun Wang, Wulong Liu, Yong Yu

In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.

Imitation Learning

Compositional ADAM: An Adaptive Compositional Solver

no code implementations10 Feb 2020 Rasul Tutunov, Minne Li, Alexander I. Cowen-Rivers, Jun Wang, Haitham Bou-Ammar

In this paper, we present C-ADAM, the first adaptive solver for compositional problems involving a non-linear functional nesting of expected values.

Meta-Learning

Gröbner-Shirshov bases for the Coxeter groups of the types $G_2,F_4,E_6,E_7$ and $E_8$

no code implementations18 Feb 2020 Jun Wang

The author is mainly interest in the Gr\"{o}bner-Shirshov bases of finite Coxeter groups.

Group Theory Rings and Algebras 16S15, 20F55

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

1 code implementation10 Mar 2020 Fei Shan, Yaozong Gao, Jun Wang, Weiya Shi, Nannan Shi, Miaofei Han, Zhong Xue, Dinggang Shen, Yuxin Shi

The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients.

COVID-19 Image Segmentation Segmentation

Partial Multi-label Learning with Label and Feature Collaboration

no code implementations17 Mar 2020 Tingting Yu, Guoxian Yu, Jun Wang, Maozu Guo

Partial multi-label learning (PML) models the scenario where each training instance is annotated with a set of candidate labels, and only some of the labels are relevant.

Multi-Label Learning

Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives

no code implementations22 Mar 2020 Jingwei Song, Shaobo Xia, Jun Wang, Dong Chen

To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives.

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

1 code implementation6 Apr 2020 Feng Shi, Jun Wang, Jun Shi, Ziyan Wu, Qian Wang, Zhenyu Tang, Kelei He, Yinghuan Shi, Dinggang Shen

In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up.

Computed Tomography (CT)

ControlVAE: Controllable Variational Autoencoder

no code implementations ICML 2020 Huajie Shao, Shuochao Yao, Dachun Sun, Aston Zhang, Shengzhong Liu, Dongxin Liu, Jun Wang, Tarek Abdelzaher

Variational Autoencoders (VAE) and their variants have been widely used in a variety of applications, such as dialog generation, image generation and disentangled representation learning.

Image Generation Language Modelling +1

CausalVAE: Structured Causal Disentanglement in Variational Autoencoder

1 code implementation CVPR 2021 Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.

counterfactual Disentanglement

Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation

1 code implementation23 Apr 2020 Jingwei Song, Shaobo Xia, Jun Wang, Mitesh Patel, Dong Chen

Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion.

Hyperspectral Image Denoising Image Denoising +1

Deep Feature-preserving Normal Estimation for Point Cloud Filtering

no code implementations24 Apr 2020 Dening Lu, Xuequan Lu, Yangxing Sun, Jun Wang

In this paper, we propose a novel feature-preserving normal estimation method for point cloud filtering with preserving geometric features.

Position

Actor-Critic Reinforcement Learning for Control with Stability Guarantee

no code implementations29 Apr 2020 Minghao Han, Lixian Zhang, Jun Wang, Wei Pan

Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation.

Motion Planning reinforcement-learning +1

A Deep Recurrent Survival Model for Unbiased Ranking

1 code implementation30 Apr 2020 Jiarui Jin, Yuchen Fang, Wei-Nan Zhang, Kan Ren, Guorui Zhou, Jian Xu, Yong Yu, Jun Wang, Xiaoqiang Zhu, Kun Gai

Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data.

Information Retrieval Position +2

Adaptive Structural Fingerprints for Graph Attention Networks

no code implementations ICLR 2020 Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang

Yet, how to fully exploit rich structural information in the attention mechanism remains a challenge.

Graph Attention

ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language

2 code implementations ECCV 2020 Zhe Wang, Zhiyuan Fang, Jun Wang, Yezhou Yang

Person search by natural language aims at retrieving a specific person in a large-scale image pool that matches the given textual descriptions.

Attribute Contrastive Learning +2

Learning from a Lightweight Teacher for Efficient Knowledge Distillation

no code implementations19 May 2020 Yuang Liu, Wei zhang, Jun Wang

Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the soft target generated by well-trained teachers.

Knowledge Distillation

A Framework for Behavioral Biometric Authentication using Deep Metric Learning on Mobile Devices

no code implementations26 May 2020 Cong Wang, Yanru Xiao, Xing Gao, Li Li, Jun Wang

We show the feasibility of training with mobile CPUs, where training 100 epochs takes less than 10 mins and can be boosted 3-5 times with feature transfer.

Metric Learning Multi-class Classification

Multi-Agent Determinantal Q-Learning

1 code implementation ICML 2020 Yaodong Yang, Ying Wen, Li-Heng Chen, Jun Wang, Kun Shao, David Mguni, Wei-Nan Zhang

Though practical, current methods rely on restrictive assumptions to decompose the centralized value function across agents for execution.

Q-Learning

Learning to Model Opponent Learning

1 code implementation6 Jun 2020 Ian Davies, Zheng Tian, Jun Wang

In this work, we develop a novel approach to modelling an opponent's learning dynamics which we term Learning to Model Opponent Learning (LeMOL).

Decision Making Multi-agent Reinforcement Learning

SAMBA: Safe Model-Based & Active Reinforcement Learning

1 code implementation12 Jun 2020 Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Abdullah, Aivar Sootla, Jun Wang, Haitham Ammar

In this paper, we propose SAMBA, a novel framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics.

Reinforcement Learning (RL) Safe Reinforcement Learning

Hyperspectral Super-Resolution via Interpretable Block-Term Tensor Modeling

no code implementations18 Jun 2020 Meng Ding, Xiao Fu, Ting-Zhu Huang, Jun Wang, Xi-Le Zhao

This work employs an idea that models spectral images as tensors following the block-term decomposition model with multilinear rank-$(L_r, L_r, 1)$ terms (i. e., the LL1 model) and formulates the HSR problem as a coupled LL1 tensor decomposition problem.

Super-Resolution Tensor Decomposition

Bidirectional Loss Function for Label Enhancement and Distribution Learning

no code implementations7 Jul 2020 Xinyuan Liu, Jihua Zhu, Qinghai Zheng, Zhongyu Li, Ruixin Liu, Jun Wang

More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.

Multi-Label Learning

InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling

no code implementations ECCV 2020 Jun Wang, Shiyi Lan, Mingfei Gao, Larry S. Davis

Results show that our framework achieves the state-of-the-art performance with 31 FPS and improves our baseline significantly by 9. 0% mAP on the nuScenes test set.

3D Object Detection Autonomous Driving +2

Semi-Siamese Training for Shallow Face Learning

3 code implementations ECCV 2020 Hang Du, Hailin Shi, Yuchi Liu, Jun Wang, Zhen Lei, Dan Zeng, Tao Mei

Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.

Face Recognition

Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search

1 code implementation ECCV 2020 Yuan Tian, Qin Wang, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search.

Generative Adversarial Network Image Generation +3

NPCFace: Negative-Positive Collaborative Training for Large-scale Face Recognition

no code implementations20 Jul 2020 Dan Zeng, Hailin Shi, Hang Du, Jun Wang, Zhen Lei, Tao Mei

However, the correlation between hard positive and hard negative is overlooked, and so is the relation between the margins in positive and negative logits.

Face Recognition

Bilevel Learning Model Towards Industrial Scheduling

no code implementations10 Aug 2020 Longkang Li, Hui-Ling Zhen, Mingxuan Yuan, Jiawen Lu, XialiangTong, Jia Zeng, Jun Wang, Dirk Schnieders

In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism.

Scheduling

Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach

no code implementations15 Aug 2020 Lei LI, Qihang Peng, Jun Wang

The first is based on multi-view convolutional neural network by treating signals from different receive antennas as different views of a 3D object and designing the location and operation of view-pooling layer that are suitable for feature fusion of multi-antenna signals.

Time irreversibility and amplitude irreversibility measures for nonequilibrium processes

no code implementations19 Aug 2020 Wenpo Yao, Jun Wang, Matjaz Perc, Wenli Yao, Jiafei Dai, Daqing Guo, Dezhong Yao

Time irreversibility should be measured based on the permutations of symmetric vectors rather than symmetric permutations, whereas symmetric permutations can instead be employed to determine the quantitative amplitude irreversibility -- a novel parameter proposed in this paper for nonequilibrium calculated by means of the probabilistic difference in amplitude fluctuations.

Liouville type theorems and periodic solutions for the nonhomogeneous parabolic systems

no code implementations30 Aug 2020 Aleks Jevnikar, Jun Wang, Wen Yang

In the present paper we derive Liouville type results and existence of periodic solutions for $\chi^{(2)}$ type systems with non-homogeneous nonlinearities.

Analysis of PDEs 35K9, 35J61, 35B45

Learning to Infer User Hidden States for Online Sequential Advertising

no code implementations3 Sep 2020 Zhaoqing Peng, Junqi Jin, Lan Luo, Yaodong Yang, Rui Luo, Jun Wang, Wei-Nan Zhang, Haiyang Xu, Miao Xu, Chuan Yu, Tiejian Luo, Han Li, Jian Xu, Kun Gai

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both important.

3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation

no code implementations15 Sep 2020 Yuanpeng Liu, Jun Zhou, Yuqi Zhang, Chao Ding, Jun Wang

To address the problem, a novel 3DPVNet is presented in this work, which utilizes 3D local patches to vote for the object 6D poses.

6D Pose Estimation

Multi-modal Summarization for Video-containing Documents

1 code implementation17 Sep 2020 Xiyan Fu, Jun Wang, Zhenglu Yang

Summarization of multimedia data becomes increasingly significant as it is the basis for many real-world applications, such as question answering, Web search, and so forth.

Question Answering Video Summarization

A Real-time Contribution Measurement Method for Participants in Federated Learning

no code implementations28 Sep 2020 Bingjie Yan, Yize Zhou, Boyi Liu, Jun Wang, Yuhan Zhang, Li Liu, Xiaolan Nie, Zhiwei Fan, Zhixuan Liang

However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.

Federated Learning

Deep Incomplete Multi-View Multiple Clusterings

no code implementations2 Oct 2020 Shaowei Wei, Jun Wang, Guoxian Yu, Carlotta Domeniconi, Xiangliang Zhang

Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering.

Clustering

Multi-typed Objects Multi-view Multi-instance Multi-label Learning

no code implementations6 Oct 2020 Yuanlin Yang, Guoxian Yu, Jun Wang, Carlotta Domeniconi, Xiangliang Zhang

Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented with heterogeneous feature views and annotated with a set of non-exclusive but semantically related labels.

Multi-Label Learning

Style Attuned Pre-training and Parameter Efficient Fine-tuning for Spoken Language Understanding

no code implementations9 Oct 2020 Jin Cao, Jun Wang, Wael Hamza, Kelly Vanee, Shang-Wen Li

The light encoder architecture separates the shared pre-trained networks from the mappings of generally encoded knowledge to specific domains of SLU, allowing for the domain adaptation to be performed solely at the light encoder and thus increasing efficiency.

Domain Adaptation Language Modelling +1

Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Network

no code implementations10 Oct 2020 Jun Wang, Qianying Liu, Haotian Xie, Zhaogang Yang, Hefeng Zhou

In this paper, the Convolutional Neutral Network (CNN) has been adapted to predict and classify lymph node metastasis in breast cancer.

Data Augmentation Image Cropping +1

Empirical or Invariant Risk Minimization? A Sample Complexity Perspective

3 code implementations ICLR 2021 Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, Kush R. Varshney

Recently, invariant risk minimization (IRM) was proposed as a promising solution to address out-of-distribution (OOD) generalization.

U-rank: Utility-oriented Learning to Rank with Implicit Feedback

no code implementations1 Nov 2020 Xinyi Dai, Jiawei Hou, Qing Liu, Yunjia Xi, Ruiming Tang, Weinan Zhang, Xiuqiang He, Jun Wang, Yong Yu

To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list.

Click-Through Rate Prediction Learning-To-Rank +2

An Overview of Multi-Agent Reinforcement Learning from Game Theoretical Perspective

1 code implementation1 Nov 2020 Yaodong Yang, Jun Wang

In this work, we provide a monograph on MARL that covers both the fundamentals and the latest developments in the research frontier.

Multi-agent Reinforcement Learning reinforcement-learning +1

A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning

no code implementations2 Nov 2020 Chang Xu, Jun Wang, Yuqing Tang, Francisco Guzman, Benjamin I. P. Rubinstein, Trevor Cohn

In this paper, we show that targeted attacks on black-box NMT systems are feasible, based on poisoning a small fraction of their parallel training data.

Data Poisoning Machine Translation +2

Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images

1 code implementation11 Nov 2020 Jingxiong Li, Yaqi Wang, Shuai Wang, Jun Wang, Jun Liu, Qun Jin, Lingling Sun

Moreover, massive data collection is impractical for a newly emerged disease, which limited the performance of data thirsty deep learning models.

Classification COVID-19 Diagnosis +1

Automated Prostate Cancer Diagnosis Based on Gleason Grading Using Convolutional Neural Network

no code implementations29 Nov 2020 Haotian Xie, Yong Zhang, Jun Wang, Jingjing Zhang, Yifan Ma, Zhaogang Yang

The Gleason grading system using histological images is the most powerful diagnostic and prognostic predictor of prostate cancer.

Data Augmentation Image Reconstruction

Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets

no code implementations NeurIPS 2020 Rui Luo, Qiang Zhang, Yaodong Yang, Jun Wang

In this paper, we present a new practical method for Bayesian learning that can rapidly draw representative samples from complex posterior distributions with multiple isolated modes in the presence of mini-batch noise.

Measuring Correlation-to-Causation Exaggeration in Press Releases

1 code implementation COLING 2020 Bei Yu, Jun Wang, Lu Guo, Yingya Li

By comparing the claims made in a press release with the corresponding claims in the original research paper, we found that 22{\%} of press releases made exaggerated causal claims from correlational findings in observational studies.

A 3D Non-Stationary Channel Model for 6G Wireless Systems Employing Intelligent Reflecting Surface

no code implementations3 Dec 2020 Yingzhuo Sun, Cheng-Xiang Wang, Jie Huang, Jun Wang

The evolution of clusters on the linear array and planar array is also considered in the proposed model.

Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions

no code implementations9 Dec 2020 Jun Wang, Shaoguo Wen, Kaixing Chen, Jianghua Yu, Xin Zhou, Peng Gao, Changsheng Li, Guotong Xie

Active learning generally involves querying the most representative samples for human labeling, which has been widely studied in many fields such as image classification and object detection.

Active Learning Image Classification +5

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

3 code implementations18 Dec 2020 Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos santos, Bing Xiang

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).

Ranked #6 on Text-To-SQL on spider (Exact Match Accuracy (Dev) metric)

Language Modelling Self-Supervised Learning +2

Learn molecular representations from large-scale unlabeled molecules for drug discovery

no code implementations21 Dec 2020 Pengyong Li, Jun Wang, Yixuan Qiao, Hao Chen, Yihuan Yu, Xiaojun Yao, Peng Gao, Guotong Xie, Sen Song

Here, we proposed a novel Molecular Pre-training Graph-based deep learning framework, named MPG, that leans molecular representations from large-scale unlabeled molecules.

Drug Discovery

Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction

no code implementations22 Dec 2020 Yao Zhang, Xu Zhang, Jun Wang, Hongru Liang, Wenqiang Lei, Zhe Sun, Adam Jatowt, Zhenglu Yang

The current methods for the link prediction taskhavetwonaturalproblems:1)the relation distributions in KGs are usually unbalanced, and 2) there are many unseen relations that occur in practical situations.

Knowledge Graphs Link Prediction +1

Causal World Models by Unsupervised Deconfounding of Physical Dynamics

no code implementations28 Dec 2020 Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang

The capability of imagining internally with a mental model of the world is vitally important for human cognition.

counterfactual

Adaptive Curriculum Learning

no code implementations ICCV 2021 Yajing Kong, Liu Liu, Jun Wang, DaCheng Tao

Therefore, in contrast to recent works using a fixed curriculum, we devise a new curriculum learning method, Adaptive Curriculum Learning (Adaptive CL), adapting the difficulty of examples to the current state of the model.

Binary Classification

Multi-Agent Trust Region Learning

1 code implementation1 Jan 2021 Ying Wen, Hui Chen, Yaodong Yang, Zheng Tian, Minne Li, Xu Chen, Jun Wang

We derive the lower bound of agents' payoff improvements for MATRL methods, and also prove the convergence of our method on the meta-game fixed points.

Atari Games Multi-agent Reinforcement Learning +3

MLVSNet: Multi-Level Voting Siamese Network for 3D Visual Tracking

1 code implementation ICCV 2021 Zhoutao Wang, Qian Xie, Yu-Kun Lai, Jing Wu, Kun Long, Jun Wang

To deal with sparsity in outdoor 3D point clouds, we propose to perform Hough voting on multi-level features to get more vote centers and retain more useful information, instead of voting only on the final level feature as in previous methods.

3D Object Detection object-detection +1

Reinforcement Learning for Control with Probabilistic Stability Guarantee

no code implementations1 Jan 2021 Minghao Han, Zhipeng Zhou, Lixian Zhang, Jun Wang, Wei Pan

Reinforcement learning is promising to control dynamical systems for which the traditional control methods are hardly applicable.

reinforcement-learning Reinforcement Learning (RL)

Regioned Episodic Reinforcement Learning

no code implementations1 Jan 2021 Jiarui Jin, Cong Chen, Ming Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Yong Yu, Jun Wang, Alex Smola

Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.

reinforcement-learning Reinforcement Learning (RL)

Learning to Explore with Pleasure

no code implementations1 Jan 2021 Yean Hoon Ong, Jun Wang

Exploration is a long-standing challenge in sequential decision problem in machine learning.

Bayesian Optimisation BIG-bench Machine Learning +1

Learning Predictive Communication by Imagination in Networked System Control

no code implementations1 Jan 2021 Yali Du, Yifan Zhao, Meng Fang, Jun Wang, Gangyan Xu, Haifeng Zhang

Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.

reinforcement-learning Reinforcement Learning (RL)

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