no code implementations • EMNLP 2021 • Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard
Using data from English cloze tests, in which subjects also self-reported their gender, age, education, and race, we examine performance differences of pretrained language models across demographic groups, defined by these (protected) attributes.
no code implementations • 22 Mar 2023 • Hao Wang, Chen Li, JinZhe Jiang, Xin Zhang, YaQian Zhao, Weifeng Gong
Recently, the robustness of deep learning models has received widespread attention, and various methods for improving model robustness have been proposed, including adversarial training, model architecture modification, design of loss functions, certified defenses, and so on.
no code implementations • 20 Mar 2023 • Xiaodong Zhao, YiXuan Luo, Juejing Liu, Wenjun Liu, Kevin M. Rosso, Xiaofeng Guo, Tong Geng, Ang Li, Xin Zhang
This study highlighted the importance of labeled experimental patterns on the training of DNN models to solve u-XRD mapping data from in-situ experiments involving liquid phase.
no code implementations • 15 Mar 2023 • Congqi Cao, Yizhe WANG, Yue Lu, Xin Zhang, Yanning Zhang
Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling.
no code implementations • 5 Mar 2023 • Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu
Decentralized min-max optimization problems with domain constraints underpins many important ML applications, including multi-agent ML fairness assurance, and policy evaluations in multi-agent reinforcement learning.
1 code implementation • 25 Feb 2023 • Yu Liu, Xin Zhang, Jingtao Ding, Yanxin Xi, Yong Li
To address such issues, in this paper, we propose a Knowledge-infused Contrastive Learning (KnowCL) model for urban imagery-based socioeconomic prediction.
1 code implementation • 20 Feb 2023 • Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, Yong Jiang, Wenjuan Han
Zero-shot information extraction (IE) aims to build IE systems from the unannotated text.
no code implementations • 17 Feb 2023 • Xin Zhang, Liangxiu Han, Lianghao Han, Haoming Chen, Darren Dancey, Daoqiang Zhang
Specifically, it consists of two primary components: 1) A fast and efficient explainable patch selection mechanism for determining the most discriminative patches based on computing the SHapley Additive exPlanations (SHAP) contribution to a transfer learning model for AD diagnosis on massive medical data; and 2) A novel patch-based network for extracting deep features and AD classfication from the selected patches with position embeddings to retain position information, capable of capturing the global and local information of inter- and intra-patches.
1 code implementation • 18 Jan 2023 • Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, Yupeng Wu
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
1 code implementation • 23 Dec 2022 • Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu
To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP.
no code implementations • 16 Dec 2022 • Congqi Cao, Xin Zhang, Shizhou Zhang, Peng Wang, Yanning Zhang
To enhance the discriminative power of features, we propose a batch clustering based loss to encourage a clustering branch to generate distinct normal and abnormal clusters based on a batch of data.
no code implementations • 4 Nov 2022 • Xin Zhang, Iván Vallés-Pérez, Andreas Stolcke, Chengzhu Yu, Jasha Droppo, Olabanji Shonibare, Roberto Barra-Chicote, Venkatesh Ravichandran
By fine-tuning an ASR model on synthetic stuttered speech we are able to reduce word error by 5. 7% relative on stuttered utterances, with only minor (<0. 2% relative) degradation for fluent utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
no code implementations • 24 Oct 2022 • Xin Zhang, Rabab Abdelfattah, Yuqi Song, Samuel A. Dauchert, XiaoFeng Wang
Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications.
no code implementations • 24 Oct 2022 • Xin Zhang, Rabab Abdelfattah, Yuqi Song, XiaoFeng Wang
Through comprehensive experiments on three large-scale multi-label image datasets, i. e. MS-COCO, NUS-WIDE, and Pascal VOC12, we show that our method can handle the imbalance between positive labels and negative labels, while still outperforming existing missing-label learning approaches in most cases, and in some cases even approaches with fully labeled datasets.
1 code implementation • 23 Oct 2022 • Panzhong Lu, Xin Zhang, Meishan Zhang, Min Zhang
First, we construct a dataset of phrase grounding with both noun phrases and pronouns to image regions.
no code implementations • 20 Oct 2022 • Rabab Abdelfattah, Xin Zhang, Mostafa M. Fouda, XiaoFeng Wang, Song Wang
To effectively address partial-label classification, this paper proposes an end-to-end Generic Game-theoretic Network (G2NetPL) for partial-label learning, which can be applied to most partial-label settings, including a very challenging, but annotation-efficient case where only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled.
Multi-Label Classification
Multi-Label Image Classification
+2
no code implementations • 2 Oct 2022 • Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu
To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as in conventional federated learning.
no code implementations • 27 Sep 2022 • Conghe Wang, Yutong He, Xia Wang, Honghao Huang, Changda Yan, Xin Zhang, Hongwei Chen
Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners.
no code implementations • 14 Sep 2022 • Xin Zhang, Qiaoyu Tan, Xiao Huang, Bo Li
Thus, blindly augmenting all graphs without considering their individual characteristics may undermine the performance of GCL arts. To deal with this, we propose the first principled framework, termed as \textit{G}raph contrastive learning with \textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL by allowing each graph to choose its own suitable augmentation operations. In essence, GPA infers tailored augmentation strategies for each graph based on its topology and node attributes via a learnable augmentation selector, which is a plug-and-play module and can be effectively trained with downstream GCL models end-to-end.
no code implementations • 8 Sep 2022 • Junhao Liu, Xin Zhang
We propose a general framework to adapt various local explanation techniques to recurrent neural networks.
no code implementations • 2 Sep 2022 • Erhan Bayraktar, Ibrahim Ekren, Xin Zhang
In this paper, we study a learning problem in which a forecaster only observes partial information.
no code implementations • 30 Aug 2022 • Shenglian Lu, Xiaoyu Liu, Zixaun He, Manoj Karkee, Xin Zhang
Results showed that the proposed Swin-T-YOLOv5 outperformed all other studied models for grape bunch detection, with up to 97% of mean Average Precision (mAP) and 0. 89 of F1-score when the weather was cloudy.
1 code implementation • COLING 2022 • Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
no code implementations • 22 Aug 2022 • Rabab Abdelfattah, Xin Zhang, Zhenyao Wu, Xinyi Wu, XiaoFeng Wang, Song Wang
A special case is to annotate only one positive label in each training image.
Multi-Label Classification
Multi-Label Image Classification
+1
no code implementations • 17 Aug 2022 • Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, Zhengyuan Zhu
Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question.
no code implementations • 17 Aug 2022 • Zhuqing Liu, Xin Zhang, Jia Liu
To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed stochastic variance-reduced optimization methods.
no code implementations • 3 Aug 2022 • Xin Zhang, Ying-Cong Chen
Domain Generalization (DG) aims to safely transfer a model to unseen target domains by only relying on a set of source domains.
no code implementations • 27 Jul 2022 • Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu
Our main contributions in this paper are two-fold: i) We first propose a deterministic algorithm called INTERACT (inner-gradient-descent-outer-tracked-gradient) that requires the sample complexity of $\mathcal{O}(n \epsilon^{-1})$ and communication complexity of $\mathcal{O}(\epsilon^{-1})$ to solve the bilevel optimization problem, where $n$ and $\epsilon > 0$ are the number of samples at each agent and the desired stationarity gap, respectively.
no code implementations • 22 Jul 2022 • Samson B. Akintoye, Liangxiu Han, Huw Lloyd, Xin Zhang, Darren Dancey, Haoming Chen, Daoqiang Zhang
Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking's problems, leading to poor performance in terms of training time.
no code implementations • 21 Jul 2022 • Xian Tao, Xinyi Gong, Xin Zhang, Shaohua Yan, Chandranath Adak
This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning.
1 code implementation • 17 Jul 2022 • Jingwei Zhang, Xin Zhang, Ke Ma, Rajarsi Gupta, Joel Saltz, Maria Vakalopoulou, Dimitris Samaras
Histopathology whole slide images (WSIs) play a very important role in clinical studies and serve as the gold standard for many cancer diagnoses.
no code implementations • 23 May 2022 • Wei Wang, Xin Zhang, Jiaqi Yi, Xianqi Liao, Wenjie Li, Zhenhong Li
The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details.
1 code implementation • 27 Apr 2022 • Xin Zhang, Xiaohua Xie, JianHuang Lai, Wei-Shi Zheng
To address this problem, we propose a framework of person retrieval based on cross-camera trajectory generation which integrates both temporal and spatial information.
1 code implementation • ACL 2022 • Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Xiaobin Wang, Min Zhang
Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy.
no code implementations • 22 Feb 2022 • Xin Zhang, Minho Jin, Roger Cheng, Ruirui Li, Eunjung Han, Andreas Stolcke
In this work, we propose contrastive-mixup, a novel augmentation strategy that learns distinguishing representations based on a distance metric.
no code implementations • 19 Feb 2022 • Zhengqing Miao, Xin Zhang, Carlo Menon, Yelong Zheng, Meirong Zhao, Dong Ming
Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15. 2%, 10. 2% respectively in IIA dataset, and 5. 5%, 4. 2% in IIB dataset.
no code implementations • 14 Feb 2022 • Congqi Cao, Xin Zhang, Shizhou Zhang, Peng Wang, Yanning Zhang
For weakly supervised anomaly detection, most existing work is limited to the problem of inadequate video representation due to the inability of modeling long-term contextual information.
no code implementations • 11 Feb 2022 • Weidong Liu, Xiaojun Mao, Xin Zhang
Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications.
1 code implementation • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2022 • Tai An, Xin Zhang, Chunlei Huo, Bin Xue, Lingfeng Wang, Chunhong Pan
In addition, TR-MISR adopts an additional learnable embedding vector that fuses these vectors to restore the details to the greatest extent. TR-MISR has successfully applied the transformer to MISR tasks for the first time, notably reducing the difficulty of training the transformer by ignoring the spatial relations of image patches.
Ranked #1 on
Multi-Frame Super-Resolution
on PROBA-V
no code implementations • 22 Dec 2021 • Sen Pei, Xin Zhang, Richard Yida Xu, Gaofeng Meng
This paper focuses on the problem of detecting out-of-distribution (ood) samples with neural nets.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
no code implementations • NeurIPS 2021 • Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu
To our knowledge, this paper is the first work that achieves both $\mathcal{O}(\epsilon^{-2})$ sample complexity and $\mathcal{O}(\epsilon^{-2})$ communication complexity in decentralized policy evaluation for cooperative MARL.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+1
1 code implementation • 25 Nov 2021 • Xin Zhang, Shixiang Shane Gu, Yutaka Matsuo, Yusuke Iwasawa
We propose Domain Prompt Learning (DPL) as a novel approach for domain inference in the form of conditional prompt generation.
Ranked #2 on
Transfer Learning
on Office-Home
no code implementations • 23 Nov 2021 • Xin Zhang, Zixuan Liu, Kaiwen Xiao, Tian Shen, Junzhou Huang, Wei Yang, Dimitris Samaras, Xiao Han
Labels are costly and sometimes unreliable.
Ranked #4 on
Image Classification
on mini WebVision 1.0
no code implementations • 22 Nov 2021 • Jing Fan, Xin Zhang, Sheng Zhang, Yan Pan, Lixiang Guo
In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations.
no code implementations • 12 Nov 2021 • Xin Zhang, Liangxiu Han, Tam Sobeih, Lewis Lappin, Mark Lee, Andew Howard, Aron Kisdi
In this work, we propose a novel deep learning framework: a self-supervised spectral-spatial attention-based vision transformer (SSVT).
no code implementations • 20 Oct 2021 • Xin Zhang, Liangxiu Han, Tam Sobeih, Lianghao Han, Nina Dempsey, Symeon Lechareas, Ascanio Tridente, Haoming Chen, Stephen White
The proposed method can provide more detailed high resolution visual explanation for the classification decision, compared to current state-of-the-art visual explanation methods and has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.
no code implementations • 5 Oct 2021 • Xin Zhang, Xiujun Shu, Bingwen Zhang, Jie Ren, Lizhou Zhou, Xin Chen
Deterministic models, such as ray tracing based on physical laws of wave propagation, are more accurate and site specific.
no code implementations • 29 Sep 2021 • Xin Zhang, Yanhua Li, Ziming Zhang, Christopher Brinton, Zhenming Liu, Zhi-Li Zhang, Hui Lu, Zhihong Tian
State-of-the-art imitation learning (IL) approaches, e. g, GAIL, apply adversarial training to minimize the discrepancy between expert and learner behaviors, which is prone to unstable training and mode collapse.
no code implementations • 23 Aug 2021 • Haibo Yang, Xin Zhang, Prashant Khanduri, Jia Liu
To satisfy the need for flexible worker participation, we consider a new FL paradigm called "Anarchic Federated Learning" (AFL) in this paper.
no code implementations • 18 Jun 2021 • Chen Li, JinZhe Jiang, Xin Zhang, Tonghuan Zhang, YaQian Zhao, Dongdong Jiang, RenGang Li
Interpreting how does deep neural networks (DNNs) make predictions is a vital field in artificial intelligence, which hinders wide applications of DNNs.
1 code implementation • ACL 2021 • Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie
Crowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers.
1 code implementation • 17 May 2021 • Kelei He, Wen Ji, Tao Zhou, Zhuoyuan Li, Jing Huo, Xin Zhang, Yang Gao, Dinggang Shen, Bing Zhang, Junfeng Zhang
Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor using the intermediate data distributions generated for the two domains, which includes an image-to-image translator and a shared-weighted segmentation network.
no code implementations • 4 May 2021 • Xin Zhang, Jia Liu, Zhengyuan Zhu, Elizabeth S. Bentley
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementation simplicity.
1 code implementation • 26 Apr 2021 • Xin Zhang, Lan Wu, Zhixue Chen
Our loss function, motivated by the long-short strategy, is endogenously shift-invariant and can be viewed as a direct generalization of ListMLE.
no code implementations • 26 Feb 2021 • Chen Li, JinZhe Jiang, YaQian Zhao, RenGang Li, EnDong Wang, Xin Zhang, Kun Zhao
Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN).
no code implementations • 18 Feb 2021 • Ling-Feng Wang, Dong-Ze He, Jing-Fei Zhang, Xin Zhang
When the TD data are added to the CMB$+$BAO$+$SN$+H_0$ data, we find that: (i) the coupling parameter $\beta$ in all the considered IDE models is positive within 1$\sigma$ range, implying a mild preference for the case of cold dark matter decaying into dark energy; (ii) the IDE model with $Q = \beta H_{0} \rho_{\rm c}$ slightly relieves the $S_8$ tension, but the other considered IDE models further aggravate this tension; (iii) the Akaike information criteria of the IDE models with $Q \propto \rho_{\rm c}$ are lower than that of the $\Lambda$CDM model, indicating that these IDE models are more preferred by the current mainstream data.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology
no code implementations • 5 Feb 2021 • Xin Zhang, Andreas Klümper, Vladislav Popkov
Bethe eigenstates are described by two complementary sets of Bethe Ansatz equations for regular roots, one for each invariant subspace.
Statistical Mechanics Mathematical Physics Mathematical Physics Quantum Physics
no code implementations • 5 Feb 2021 • Vladislav Popkov, Xin Zhang, Andreas Klümper
The phantom Bethe roots lead to degeneracies between different magnetization sectors in the periodic case and to the appearance of spin helix states (SHS), i. e. periodically modulated states of chiral nature in both open and closed systems.
Statistical Mechanics Mathematical Physics Mathematical Physics Quantum Physics
no code implementations • 26 Jan 2021 • Li-Yang Gao, She-Sheng Xue, Xin Zhang
Here we explain this model as a scenario of vacuum energy interacting with matter and radiation.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology
no code implementations • 14 Jan 2021 • Xin Zhang, Guofeng Yang, Mengqi Yan, Lay Kee Ang, Yee Sin Ang
Energy harvesting from sun and outer space using thermoradiative devices (TRD), despite being promising renewable energy sources, are limited only to daytime and nighttime period, respectively.
Applied Physics Mesoscale and Nanoscale Physics
no code implementations • 7 Jan 2021 • Xingchen Zhou, Yan Gong, Xian-Min Meng, Xin Zhang, Ye Cao, Xuelei Chen, Valeria Amaro, Zuhui Fan, Liping Fu
This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys.
Photometric Redshift Estimation
Cosmology and Nongalactic Astrophysics
no code implementations • 22 Dec 2020 • Shiqi Sheng, Haijun Yang, Liuhua Mu, Zixin Wang, Jihong Wang, Peng Xiu, Jun Hu, Xin Zhang, Feng Zhang, Haiping Fang
We experimentally demonstrated that the AYFFF self-assemblies adsorbed with various monovalent cations (Na+, K+, and Li+) show unexpectedly super strong paramagnetism.
Biological Physics
no code implementations • 17 Dec 2020 • Erhan Bayraktar, Suman Chakraborty, Xin Zhang
Keeping an edge $(i, j)$ of $G_n$ with probability $\min \{ {a^n_{i, j}}/{n}, 1 \}$ independently, we obtain a sequence of random graphs $G_n(\frac{1}{n})$.
Probability Combinatorics
no code implementations • NeurIPS 2020 • Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang
This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency?
no code implementations • 31 Oct 2020 • Erhan Bayraktar, Ibrahim Ekren, Xin Zhang
We study the problem of prediction with expert advice with adversarial corruption where the adversary can at most corrupt one expert.
no code implementations • 31 Oct 2020 • Bingxu Li, Fanyong Cheng, Xin Zhang, Can Cui, Wenjian Cai
Usually, the number of labeled data is limited and most data available are unlabeled.
1 code implementation • 2 Oct 2020 • Xin Zhang, Yanhua Li, Ziming Zhang, Zhi-Li Zhang
This naturally gives rise to the following question: Given a set of expert demonstrations, which divergence can recover the expert policy more accurately with higher data efficiency?
1 code implementation • 23 Sep 2020 • Sheng Zhang, Xin Zhang, Weiming Zhang, Anders Søgaard
Multi-task transfer learning based on pre-trained language encoders achieves state-of-the-art performance across a range of tasks.
no code implementations • 10 Aug 2020 • Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang
Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis.
no code implementations • 20 Jun 2020 • Tianqi Xiang, Yaxin Wang, Huiwen Li, Boren Guo, Xin Zhang
A novel location-aware beamforming scheme for millimeter wave communication is proposed for line of sight (LOS) and low mobility scenarios, in which computer vision is introduced to derive the required position or spatial angular information from the image or video captured by camera(s) co-located with mmWave antenna array at base stations.
no code implementations • 15 Jun 2020 • Xin Zhang, Ning Jia, Ioannis Ivrissimtzis
Our results show that the effect of the illumination model is important, comparable in significance to the network architecture.
no code implementations • 14 Jun 2020 • Tianqi Xiang, Huiwen Li, Boren Guo, Xin Zhang
The proposed exposure avoidance method is validated in simulations, and the results show that the finer beam management granularity can guarantee communication quality while reducing the electromagnetic exposure.
no code implementations • 2 Jun 2020 • JinZhe Jiang, Xin Zhang, Chen Li, YaQian Zhao, RenGang Li
In this model, we mapped the feature data to a quantum state in Hilbert space firstly, and then implement unitary evolution on it, in the end, we can get the classification result by im-plement measurement on the quantum state.
no code implementations • 21 May 2020 • Kelei He, Chunfeng Lian, Bing Zhang, Xin Zhang, Xiaohuan Cao, Dong Nie, Yang Gao, Junfeng Zhang, Dinggang Shen
In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.
no code implementations • 18 Mar 2020 • Erhan Bayraktar, H. Vincent Poor, Xin Zhang
We assume that one of the experts is honest and makes correct prediction with probability $\mu$ at each round.
no code implementations • 9 Mar 2020 • Bei Niu, Bi Li, Xin Zhang
An equitable tree-$k$-coloring of a graph is a vertex $k$-coloring such that each color class induces a forest and the size of any two color classes differ by at most one.
Combinatorics Discrete Mathematics
no code implementations • 12 Jan 2020 • Xin Zhang, Minghong Fang, Jia Liu, Zhengyuan Zhu
In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing.
no code implementations • 18 Dec 2019 • Wenfeng Feng, Xin Zhang, Guangpeng Zhao
Our architecture is a natural extension to ResNet, and can be integrated with existing state-of-the-art methods with little effort.
no code implementations • 22 Nov 2019 • Erhan Bayraktar, Ibrahim Ekren, Xin Zhang
We explicitly solve the nonlinear PDE that is the continuous limit of dynamic programming of \emph{expert prediction problem} in finite horizon setting with $N=4$ experts.
1 code implementation • 8 Nov 2019 • Muhammad Baqer Mollah, Jun Zhao, Dusit Niyato, Kwok-Yan Lam, Xin Zhang, Amer M. Y. M. Ghias, Leong Hai Koh, Lei Yang
In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid.
Cryptography and Security Distributed, Parallel, and Cluster Computing Networking and Internet Architecture Social and Information Networks Systems and Control Systems and Control
1 code implementation • Ecological Informatics 2019 • Xin Zhang, Aibin Chen, Guoxiong Zhou, Zhiqiang Zhang, Xibei Huang, Xiaohu Qiang
Inspired by that bird sound has various frequency distributions and continuous time-varying properties, a novel method is proposed for the classification of bird sound based on continuous frame sequence and spectrogram-frame linear network (SFLN).
no code implementations • 1 Nov 2019 • Songbin Xu, Yang Xue, Xin Zhang, Lianwen Jin
As a new way of human-computer interaction, inertial sensor based in-air handwriting can provide a natural and unconstrained interaction to express more complex and richer information in 3D space.
no code implementations • 25 Sep 2019 • Xin Zhang, Weixiao Huang, Renjie Liao, Yanhua Li
Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model.
1 code implementation • 24 Sep 2019 • Dongling Xiao, Chang Liu, Qi. Wang, Chao Wang, Xin Zhang
For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required.
no code implementations • 10 Sep 2019 • Haibo Yang, Xin Zhang, Minghong Fang, Jia Liu
In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who could send arbitrary information to the parameter server to disrupt the training process.
no code implementations • 2 Jul 2019 • Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang
Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years.
no code implementations • 30 Jun 2019 • Xin Zhang, An Yang, Sujian Li, Yizhong Wang
Machine reading comprehension aims to teach machines to understand a text like a human and is a new challenging direction in Artificial Intelligence.
no code implementations • 28 May 2019 • Xin Zhang, Jia Liu, Zhengyuan Zhu
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the network.
no code implementations • 23 May 2019 • Xin Zhang, Ning Jia, Ioannis Ivrissimtzis
We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process of creating large amounts of hand labelled real data or the need to generate photorealistic synthetic data.
no code implementations • 5 Apr 2019 • Xin Zhang, Zhengyuan Zhu
We develop theoretical properties of the method which indicates that asymptotically SCUSUM can reach high classification accuracy.
no code implementations • 25 Mar 2019 • Zenna Tavares, Xin Zhang, Edgar Minaysan, Javier Burroni, Rajesh Ranganath, Armando Solar Lezama
The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas.
no code implementations • 16 Mar 2019 • Yang Lou, Shiu Yin Yuen, Guanrong Chen, Xin Zhang
The entire on-line search history of cNrGA is stored in a binary space partitioning (BSP) tree, which is effective for performing local search.
1 code implementation • 2 Dec 2018 • Osbert Bastani, Xin Zhang, Armando Solar-Lezama
As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities.
no code implementations • 19 Nov 2018 • Xin Zhang, Qian Wang, Toby Breckon, Ioannis Ivrissimtzis
We present a method for reading digital data embedded in planar 3D printed surfaces.
1 code implementation • 17 Nov 2018 • Chenyang Li, Xin Zhang, Lufan Liao, Lianwen Jin, Weixin Yang
In this paper, we first leverage a robust feature descriptor, path signature (PS), and propose three PS features to explicitly represent the spatial and temporal motion characteristics, i. e., spatial PS (S_PS), temporal PS (T_PS) and temporal spatial PS (T_S_PS).
Ranked #1 on
Gesture Recognition
on ChaLearn 2013
no code implementations • 24 May 2018 • Xin Zhang, Jia Liu, Zhengyuan Zhu
Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning.
no code implementations • 18 Mar 2018 • Xin Zhang, Bingfang Wu, Liang Zhu, Fuyou Tian, Miao Zhang, Yuanzeng
In this paper, we first test the state of the art semantic segmentation deep learning classifiers for LUCC mapping with 7 categories in the TGRA area with rapideye 5m resolution data.
no code implementations • NeurIPS 2018 • Xin Zhang, Armando Solar-Lezama, Rishabh Singh
We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output.
no code implementations • CIKM 2017 • Jiajun Cheng, Shenglin Zhao, Jiani Zhang, Irwin King, Xin Zhang, Hui Wang
However, the prior work only attends to the sentiment information and ignores the aspect-related information in the text, which may cause mismatching between the sentiment words and the aspects when an unrelated sentiment word is semantically meaningful for the given aspect.
no code implementations • 2 Nov 2017 • Xin Zhang, Weixuan Kou, Eric I-Chao Chang, He Gao, Yubo Fan, Yan Xu
The feature learning framework is designed to extract low- and mid-level features.
Automatic Sleep Stage Classification
General Classification
+1
no code implementations • SEMEVAL 2017 • Sheng Zhang, Jiajun Cheng, Hui Wang, Xin Zhang, Pei Li, Zhaoyun Ding
We describes deep neural networks frameworks in this paper to address the community question answering (cQA) ranking task (SemEval-2017 task 3).
no code implementations • 22 Nov 2016 • Yan Xu, Zhengyang Shen, Xin Zhang, Yifan Gao, Shujian Deng, Yipei Wang, Yubo Fan, Eric I-Chao Chang
This paper proposes a multi-level feature learning framework for human action recognition using a single body-worn inertial sensor.
no code implementations • 2 Aug 2016 • Xin Zhang, Scott A. Sisson
In this article, we introduce a blocking scheme to the collapsed Gibbs sampler for the LDA model which can, with a theoretical guarantee, improve chain mixing efficiency.
114 code implementations • 25 Apr 2016 • Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, Karol Zieba
The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal.
no code implementations • 7 Nov 2015 • Xiaorui Liu, Yichao Huang, Xin Zhang, Lianwen Jin
We introduce a new pipeline for hand localization and fingertip detection.
no code implementations • 18 Dec 2014 • Changsheng Li, Fan Wei, Weishan Dong, Qingshan Liu, Xiangfeng Wang, Xin Zhang
MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement.
no code implementations • 16 Dec 2014 • Changsheng Li, Qingshan Liu, Weishan Dong, Xin Zhang, Lin Yang
In this paper, we propose a new max-margin based discriminative feature learning method.