no code implementations • NeurIPS 2011 • Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis
However, the problem then becomes finding the appropriate kernel function.
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.
no code implementations • 26 Apr 2013 • Leon Derczynski, Richard Shaw, Ben Solway, Jun Wang
Question answering involves developing methods to extract useful information from large collections of documents.
no code implementations • 12 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.
no code implementations • 20 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
2 code implementations • 25 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
no code implementations • 19 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.
no code implementations • 25 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.
no code implementations • 8 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.
no code implementations • 17 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.
no code implementations • 21 Sep 2015 • Zuxuan Wu, Yu-Gang Jiang, Xi Wang, Hao Ye, xiangyang xue, Jun Wang
A multi-stream framework is proposed to fully utilize the rich multimodal information in videos.
no code implementations • 4 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.
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.
no code implementations • 11 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.
5 code implementations • 11 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.
Ranked #2 on Click-Through Rate Prediction on Company*
1 code implementation • 3 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
no code implementations • 16 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.
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.
no code implementations • 22 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.
23 code implementations • 18 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.
Ranked #2 on Text Generation on Chinese Poems
1 code implementation • 7 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
11 code implementations • 1 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.
Ranked #1 on Click-Through Rate Prediction on iPinYou
1 code implementation • 14 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.
no code implementations • 2 Dec 2016 • Nurjahan Begum, Liudmila Ulanova, Hoang Anh Dau, Jun Wang, Eamonn Keogh
Clustering time series under DTW remains a computationally expensive operation.
1 code implementation • 10 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.
2 code implementations • ICLR 2018 • Zhiming Zhou, Han Cai, Shu Rong, Yuxuan Song, Kan Ren, Wei-Nan Zhang, Yong Yu, Jun Wang
Our proposed model also outperforms the baseline methods in the new metric.
2 code implementations • 29 Mar 2017 • Peng Peng, Ying Wen, Yaodong Yang, Quan Yuan, Zhenkun Tang, Haitao Long, Jun Wang
Many artificial intelligence (AI) applications often require multiple intelligent agents to work in a collaborative effort.
no code implementations • 28 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.
3 code implementations • 30 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.
no code implementations • 5 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.
3 code implementations • 16 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.
Ranked #140 on Image Classification on CIFAR-10
no code implementations • 5 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.
1 code implementation • 6 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.
no code implementations • 13 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.
6 code implementations • 24 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.
Ranked #1 on Text Generation on COCO Captions
no code implementations • 25 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.
no code implementations • 2 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.
no code implementations • 29 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.
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.
no code implementations • 30 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.
3 code implementations • 2 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
1 code implementation • 8 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
no code implementations • 31 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.
no code implementations • 15 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.
1 code implementation • 6 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.
3 code implementations • ICML 2018 • Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Wei-Nan Zhang, Jun Wang
Existing multi-agent reinforcement learning methods are limited typically to a small number of agents.
no code implementations • 27 Feb 2018 • Junqi Jin, Chengru Song, Han Li, Kun Gai, Jun Wang, Wei-Nan Zhang
Real-time advertising allows advertisers to bid for each impression for a visiting user.
no code implementations • 1 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.
no code implementations • 6 Mar 2018 • Jingwei Song, Jun Wang, Liang Zhao, Shoudong Huang, Gamini Dissanayake
Idled CPU is used to perform ORB- SLAM for providing robust global pose.
no code implementations • 7 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.
no code implementations • 15 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.
no code implementations • 21 Apr 2018 • Jihua Zhu, Siyu Xu, Zutao Jiang, Shanmin Pang, Jun Wang, Zhongyu Li
This paper proposes a global approach for the multi-view registration of unordered range scans.
no code implementations • 17 May 2018 • Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
no code implementations • 2 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.
1 code implementation • COLING 2018 • Hongru Liang, Haozheng Wang, Jun Wang, ShaoDi You, Zhe Sun, Jin-Mao Wei, Zhenglu Yang
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others.
1 code implementation • 11 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.
no code implementations • 10 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?
no code implementations • 11 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
no code implementations • 10 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.
no code implementations • 5 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.
1 code implementation • 9 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.
no code implementations • 14 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.
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.
no code implementations • 15 Dec 2018 • Guanghua Pan, Jun Wang, Rendong Ying, Peilin Liu
Deep learning on point clouds has made a lot of progress recently.
no code implementations • 9 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.
no code implementations • 14 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.
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.
no code implementations • 16 Jan 2019 • Jun Wang, Sujoy Sikdar, Tyler Shepherd, Zhibing Zhao, Chunheng Jiang, Lirong Xia
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
no code implementations • 26 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.
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
1 code implementation • 30 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.
no code implementations • 4 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.
no code implementations • 22 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.
no code implementations • 4 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.
no code implementations • 16 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.
no code implementations • 24 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.
no code implementations • 10 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.
no code implementations • 11 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.
no code implementations • 11 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.
no code implementations • 13 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.
no code implementations • 13 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.
no code implementations • 13 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.
no code implementations • 14 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).
no code implementations • 15 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.
no code implementations • 16 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.
1 code implementation • 17 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
no code implementations • 22 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).
no code implementations • 27 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
no code implementations • 29 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.
no code implementations • 29 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.
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.
no code implementations • WS 2019 • Beiming Cao, Nordine Sebkhi, Ted Mau, Omer T. Inan, Jun Wang
Articulation-to-speech (ATS) synthesis is a software design in SSI that directly converts articulatory movement information into audible speech signals.
1 code implementation • 12 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.
1 code implementation • 17 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.
no code implementations • 22 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.
no code implementations • 30 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.
no code implementations • 19 Aug 2019 • Dagui Chen, Junqi Jin, Wei-Nan Zhang, Fei Pan, Lvyin Niu, Chuan Yu, Jun Wang, Han Li, Jian Xu, Kun Gai
We refer to this process as Leverage.
no code implementations • 19 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.
1 code implementation • 27 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).
no code implementations • 3 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.
1 code implementation • 8 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
no code implementations • 15 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.
no code implementations • 25 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.
no code implementations • 25 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.
no code implementations • 25 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.
no code implementations • 11 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.
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.
2 code implementations • 18 Oct 2019 • Ignavier Ng, Shengyu Zhu, Zhuangyan Fang, Haoyang Li, Zhitang Chen, Jun Wang
This paper studies the problem of learning causal structures from observational data.
no code implementations • 28 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.
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.
no code implementations • 7 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.
1 code implementation • 7 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.
no code implementations • 13 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.
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.
no code implementations • 26 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.
no code implementations • 28 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.
no code implementations • 3 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.
no code implementations • IEEE Real-Time Systems Symposium (RTSS) 2019 • Ashkan Farhangi, Jiang Bian, Jun Wang, Zhishan Guo
Under the big data era, there is a crucial need to improve the performance of storage systems for data-intensive applications.
1 code implementation • 9 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
no code implementations • 24 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).
no code implementations • 27 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.
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.
no code implementations • 10 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.
no code implementations • 11 Feb 2020 • Junjie Sheng, Xiangfeng Wang, Bo Jin, Junchi Yan, Wenhao Li, Tsung-Hui Chang, Jun Wang, Hongyuan Zha
This work explores the large-scale multi-agent communication mechanism under a multi-agent reinforcement learning (MARL) setting.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 18 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
no code implementations • 3 Mar 2020 • Ziling Wu, Ping Liu, Zheng Hu, Bocheng Li, Jun Wang
Our methods can significantly reduce the cost of development and maintenance of anomaly detection.
1 code implementation • 10 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.
no code implementations • 17 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.
no code implementations • 22 Mar 2020 • Jingwei Song, Jun Wang, Liang Zhao, Shoudong Huang, Gamini Dissanayake
Our SLAM system can: (1) Incrementally build a live model by progressively fusing new observations with vivid accurate texture.
Dynamic Reconstruction Simultaneous Localization and Mapping
no code implementations • 22 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.
1 code implementation • 6 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.
no code implementations • 7 Apr 2020 • Qinghai Zheng, Jihua Zhu, Zhongyu Li, Shanmin Pang, Jun Wang, Lei Chen
The complementary graph regularizer investigates the specific information of multiple views.
1 code implementation • CVPR 2020 • Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Yiming Zhang, Kai Xu, Jun Wang
We demonstrate these by capturing contextual information at patch, object and scene levels.
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.
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.
1 code implementation • 23 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.
no code implementations • 24 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.
no code implementations • 29 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.
1 code implementation • 30 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.
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.
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.
Ranked #17 on Text based Person Retrieval on CUHK-PEDES
no code implementations • 15 May 2020 • Zhe Wang, Jun Wang, Yezhou Yang
Pedestrian detection has been heavily studied in the last decade due to its wide application.
no code implementations • 19 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.
no code implementations • 26 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.
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.
1 code implementation • 6 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).
1 code implementation • 12 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.
no code implementations • 18 Jun 2020 • Shuai Zhang, Xiaoyan Xin, Yang Wang, Yachong Guo, Qiuqiao Hao, Xianfeng Yang, Jun Wang, Jian Zhang, Bing Zhang, Wei Wang
The model provides automated recognition of given scans and generation of reports.
no code implementations • 18 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.
no code implementations • 29 Jun 2020 • Kai Zhang, Yaokang Zhu, Jun Wang, Jie Zhang, Hongyuan Zha
Graph neural networks are promising architecture for learning and inference with graph-structured data.
no code implementations • 7 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.
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.
Ranked #328 on 3D Object Detection on nuScenes
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.
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.
Ranked #13 on Image Generation on STL-10
no code implementations • 20 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.
no code implementations • 10 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.
no code implementations • 15 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.
no code implementations • 19 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.
no code implementations • 30 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
no code implementations • 3 Sep 2020 • Bin Huang, Yuanyang Du, Shuai Zhang, Wenfei Li, Jun Wang, Jian Zhang
RNAs play crucial and versatile roles in biological processes.
no code implementations • 3 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.
no code implementations • 15 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.
1 code implementation • 17 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.
no code implementations • 28 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.
no code implementations • 2 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.
no code implementations • 6 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.
no code implementations • 9 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.
no code implementations • 10 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.
3 code implementations • 19 Oct 2020 • Ming Zhou, Jun Luo, Julian Villella, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander Cowen Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang
We open-source the SMARTS platform and the associated benchmark tasks and evaluation metrics to encourage and empower research on multi-agent learning for autonomous driving.
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.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Zhen Wang, Siwei Rao, Jie Zhang, Zhen Qin, Guangjian Tian, Jun Wang
However, question generation is actually a one-to-many problem, as it is possible to raise questions with different focuses on contexts and various means of expression.
no code implementations • 1 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.
1 code implementation • 1 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
no code implementations • 2 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.
1 code implementation • 11 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.
no code implementations • 13 Nov 2020 • Minghao Han, Yuan Tian, Lixian Zhang, Jun Wang, Wei Pan
In comparison with the existing RL algorithms, the proposed method can achieve superior performance in terms of maintaining safety.
no code implementations • 29 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.
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.
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.
no code implementations • 3 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.
3 code implementations • 7 Dec 2020 • Alexander I. Cowen-Rivers, Wenlong Lyu, Rasul Tutunov, Zhi Wang, Antoine Grosnit, Ryan Rhys Griffiths, Alexandre Max Maraval, Hao Jianye, Jun Wang, Jan Peters, Haitham Bou Ammar
Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
Ranked #1 on Hyperparameter Optimization on Bayesmark
no code implementations • 9 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.
no code implementations • 11 Dec 2020 • Fitash Ul Haq, Donghwan Shin, Lionel C. Briand, Thomas Stifter, Jun Wang
In this paper, we present an approach to automatically generate test data for KP-DNNs using many-objective search.
1 code implementation • 15 Dec 2020 • Antoine Grosnit, Alexander I. Cowen-Rivers, Rasul Tutunov, Ryan-Rhys Griffiths, Jun Wang, Haitham Bou-Ammar
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
3 code implementations • 18 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)
no code implementations • 21 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.
no code implementations • 22 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.
no code implementations • 28 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.
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.
1 code implementation • 1 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.
no code implementations • ICCV 2021 • Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Dening Lu, Mingqiang Wei, Jun Wang
Hough voting, as has been demonstrated in VoteNet, is effective for 3D object detection, where voting is a key step.
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.
no code implementations • 1 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.
no code implementations • 1 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.
no code implementations • 1 Jan 2021 • Yean Hoon Ong, Jun Wang
Exploration is a long-standing challenge in sequential decision problem in machine learning.
no code implementations • 1 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.