Search Results for author: Xiang Zhang

Found 130 papers, 53 papers with code

A Unified Framework for Fair Spectral Clustering With Effective Graph Learning

no code implementations23 Nov 2023 Xiang Zhang, Qiao Wang

Traditional fair spectral clustering (FSC) methods consist of two consecutive stages, i. e., performing fair spectral embedding on a given graph and conducting $k$means to obtain discrete cluster labels.

Clustering Fairness +2

DyExplainer: Explainable Dynamic Graph Neural Networks

no code implementations25 Oct 2023 Tianchun Wang, Dongsheng Luo, Wei Cheng, Haifeng Chen, Xiang Zhang

Dynamic GNNs, with their ever-evolving graph structures, pose a unique challenge and require additional efforts to effectively capture temporal dependencies and structural relationships.

Contrastive Learning Link Prediction

Contrast Everything: A Hierarchical Contrastive Framework for Medical Time-Series

1 code implementation21 Oct 2023 Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang

The results demonstrate that COMET consistently outperforms all baselines, particularly in setup with 10% and 1% labeled data fractions across all datasets.

Contrastive Learning EEG +3

Lost in Translation: When GPT-4V(ision) Can't See Eye to Eye with Text. A Vision-Language-Consistency Analysis of VLLMs and Beyond

no code implementations19 Oct 2023 Xiang Zhang, Senyu Li, Zijun Wu, Ning Shi

Expanding on our findings, we introduce "Vision Description Prompting," a method that effectively improves performance in challenging vision-related tasks.

Image Captioning Language Modelling +2

Learning Graph Filters for Spectral GNNs via Newton Interpolation

no code implementations16 Oct 2023 Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang

Spectral Graph Neural Networks (GNNs) are gaining attention because they can surpass the limitations of message-passing GNNs by learning spectral filters that capture essential frequency information in graph data through task supervision.

Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

no code implementations13 Oct 2023 Huili Cai, Xiang Zhang, Xiaofeng Liu

The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier.

Classification Contrastive Learning +3

Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Physics Simulation

no code implementations7 Oct 2023 Yuqi Xiang, Feitong Chen, Qinsi Wang, Yang Gang, Xiang Zhang, Xinghao Zhu, Xingyu Liu, Lin Shao

In this work, we introduce $\textit{Diff-Transfer}$, a novel framework leveraging differentiable physics simulation to efficiently transfer robotic skills.


Certifiably Robust Graph Contrastive Learning

1 code implementation5 Oct 2023 Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang

Extensive experiments on real-world datasets demonstrate the effectiveness of our proposed method in providing effective certifiable robustness and enhancing the robustness of any GCL model.

Contrastive Learning Graph Representation Learning

Dynamic DAG Discovery for Interpretable Imitation Learning

no code implementations30 Sep 2023 ianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen

After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.

Causal Discovery Imitation Learning

CrossZoom: Simultaneously Motion Deblurring and Event Super-Resolving

1 code implementation29 Sep 2023 Chi Zhang, Xiang Zhang, Mingyuan Lin, Cheng Li, Chu He, Wen Yang, Gui-Song Xia, Lei Yu

Even though the collaboration between traditional and neuromorphic event cameras brings prosperity to frame-event based vision applications, the performance is still confined by the resolution gap crossing two modalities in both spatial and temporal domains.

Deblurring Event-based vision

Random Walk on Multiple Networks

1 code implementation4 Jul 2023 Dongsheng Luo, Yuchen Bian, Yaowei Yan, Xiong Yu, Jun Huan, Xiao Liu, Xiang Zhang

To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.

Link Prediction Local Community Detection +1

Interpretable Sparsification of Brain Graphs: Better Practices and Effective Designs for Graph Neural Networks

1 code implementation26 Jun 2023 Gaotang Li, Marlena Duda, Xiang Zhang, Danai Koutra, Yujun Yan

Based on these insights, we propose a new model, Interpretable Graph Sparsification (IGS), which enhances graph classification performance by up to 5. 1% with 55. 0% fewer edges.

Graph Classification

Skill Disentanglement for Imitation Learning from Suboptimal Demonstrations

1 code implementation13 Jun 2023 Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen

Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.

Disentanglement Imitation Learning

MotionTrack: Learning Motion Predictor for Multiple Object Tracking

no code implementations5 Jun 2023 Changcheng Xiao, Qiong Cao, Yujie Zhong, Long Lan, Xiang Zhang, Huayue Cai, Zhigang Luo, DaCheng Tao

Despite these developments, the task of accurately tracking objects in scenarios with homogeneous appearance and heterogeneous motion remains challenging due to the insufficient discriminability of ReID features and the predominant use of linear motion models in MOT.

Ranked #10 on Multi-Object Tracking on DanceTrack (using extra training data)

motion prediction Multi-Object Tracking +1

ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

1 code implementation29 May 2023 Xiang Zhang, Yan Lu, Huan Yan, Jingyang Huang, Yusheng Ji, Yu Gu

To further enhance the reliability of our noise decision results, ReSup uses two networks to jointly achieve noise suppression.

Facial Expression Recognition Facial Expression Recognition (FER)

GC-Flow: A Graph-Based Flow Network for Effective Clustering

1 code implementation26 May 2023 Tianchun Wang, Farzaneh Mirzazadeh, Xiang Zhang, Jie Chen

Graph convolutional networks (GCNs) are \emph{discriminative models} that directly model the class posterior $p(y|\mathbf{x})$ for semi-supervised classification of graph data.

Clustering Representation Learning

Power Grid Transient Analysis via Open-Source Circuit Simulator: A Case Study of HVDC

no code implementations16 May 2023 Yongli Zhu, Xiang Zhang, Renchang Dai

This paper proposes an electronic circuit simulator-based method to accelerate the power system transient simulation, where the modeling of a generic HVDC (High Voltage Direct Current) system is focused.

Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events

no code implementations14 Apr 2023 Yangguang Wang, Xiang Zhang, Mingyuan Lin, Lei Yu, Boxin Shi, Wen Yang, Gui-Song Xia

Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem due to the missing temporal dynamic information in both RS intra-frame scanlines and inter-frame exposures, particularly when prior knowledge about camera/object motions is unavailable.

Self-Supervised Learning

Recovering Continuous Scene Dynamics from A Single Blurry Image with Events

no code implementations5 Apr 2023 Zhangyi Cheng, Xiang Zhang, Lei Yu, Jianzhuang Liu, Wen Yang, Gui-Song Xia

This paper aims at demystifying a single motion-blurred image with events and revealing temporally continuous scene dynamics encrypted behind motion blurs.

Image Restoration SSIM

Enhancing Clinical Evidence Recommendation with Multi-Channel Heterogeneous Learning on Evidence Graphs

no code implementations3 Apr 2023 Maolin Luo, Xiang Zhang

Clinical evidence encompasses the associations and impacts between patients, interventions (such as drugs or physiotherapy), problems, and outcomes.

Decision Making Knowledge Graphs

Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding

no code implementations ICCV 2023 Xiang Zhang, Taoyue Wang, Xiaotian Li, Huiyuan Yang, Lijun Yin

This is because such pairs inevitably encode the subject-ID information, and the randomly constructed pairs may push similar facial images away due to the limited number of subjects in facial behavior datasets.

Contrastive Learning Facial Expression Recognition

Time Series Contrastive Learning with Information-Aware Augmentations

1 code implementation21 Mar 2023 Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Yuncong Chen, Haifeng Chen, Xiang Zhang

A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.

Contrastive Learning Open-Ended Question Answering +2

Learning to Super-Resolve Blurry Images with Events

1 code implementation27 Feb 2023 Lei Yu, Bishan Wang, Xiang Zhang, Haijian Zhang, Wen Yang, Jianzhuang Liu, Gui-Song Xia

Super-Resolution from a single motion Blurred image (SRB) is a severely ill-posed problem due to the joint degradation of motion blurs and low spatial resolution.

Sparse Learning Super-Resolution

Unnoticeable Backdoor Attacks on Graph Neural Networks

1 code implementation11 Feb 2023 Enyan Dai, Minhua Lin, Xiang Zhang, Suhang Wang

In particular, backdoor attack poisons the graph by attaching triggers and the target class label to a set of nodes in the training graph.

Backdoor Attack Graph Classification +1

Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective

no code implementations25 Jan 2023 Jiayuan Chen, Xiang Zhang, Yinfei Xu, Tianli Zhao, Renjie Xie, Wei Xu

Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE.

Variational Inference

Graph Topology Learning Under Privacy Constraints

no code implementations17 Jan 2023 Xiang Zhang

Towards this end, we propose a framework where personalized graphs for local clients as well as a consensus graph are jointly learned.

Graph Learning

Faithful and Consistent Graph Neural Network Explanations with Rationale Alignment

no code implementations7 Jan 2023 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Instance-level GNN explanation aims to discover critical input elements, like nodes or edges, that the target GNN relies upon for making predictions.

Inductive Bias

Uni-3D: A Universal Model for Panoptic 3D Scene Reconstruction

1 code implementation ICCV 2023 Xiang Zhang, Zeyuan Chen, Fangyin Wei, Zhuowen Tu

Performing holistic 3D scene understanding from a single-view observation, involving generating instance shapes and 3D scene segmentation, is a long-standing challenge.

3D Scene Reconstruction Image Segmentation +4

Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity

no code implementations ICCV 2023 Xiaotian Li, Xiang Zhang, Taoyue Wang, Lijun Yin

By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network.

Knowledge Distillation

TopoImb: Toward Topology-level Imbalance in Learning from Graphs

no code implementations16 Dec 2022 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

To address this problem, we propose a new framework {\method} and design (1 a topology extractor, which automatically identifies the topology group for each instance with explicit memory cells, (2 a training modulator, which modulates the learning process of the target GNN model to prevent the case of topology-group-wise under-representation.

Learning to See Through with Events

no code implementations5 Dec 2022 Lei Yu, Xiang Zhang, Wei Liao, Wen Yang, Gui-Song Xia

Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring out off-focus foreground occlusions while recovering in-focus occluded scenes from multi-view images, its performance is often deteriorated by dense occlusions and extreme lighting conditions.

FIXED: Frustratingly Easy Domain Generalization with Mixup

1 code implementation7 Nov 2022 Wang Lu, Jindong Wang, Han Yu, Lei Huang, Xiang Zhang, Yiqiang Chen, Xing Xie

Firstly, Mixup cannot effectively identify the domain and class information that can be used for learning invariant representations.

Domain Generalization Image Classification +2

Personalized Federated Learning via Heterogeneous Modular Networks

1 code implementation26 Oct 2022 Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.

Personalized Federated Learning

HP-GMN: Graph Memory Networks for Heterophilous Graphs

1 code implementation15 Oct 2022 Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang

Graph neural networks (GNNs) have achieved great success in various graph problems.

Omni-directional Pathloss Measurement Based on Virtual Antenna Array with Directional Antennas

no code implementations7 Aug 2022 Mengting Li, Fengchun Zhang, Xiang Zhang, Yejian Lyu, Wei Fan

Omni-directional pathloss, which refers to the pathloss when omni-directional antennas are used at the link ends, are essential for system design and evaluation.

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

no code implementations3 Aug 2022 Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, Suhang Wang

Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.

Link Prediction Representation Learning

Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency

1 code implementation17 Jun 2022 Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, Marinka Zitnik

Experiments against eight state-of-the-art methods show that TF-C outperforms baselines by 15. 4% (F1 score) on average in one-to-one settings (e. g., fine-tuning an EEG-pretrained model on EMG data) and by 8. 4% (precision) in challenging one-to-many settings (e. g., fine-tuning an EEG-pretrained model for either hand-gesture recognition or mechanical fault prediction), reflecting the breadth of scenarios that arise in real-world applications.

Domain Adaptation EEG +7

Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks

no code implementations10 Jun 2022 Tianxiang Zhao, Xiang Zhang, Suhang Wang

In many real-world scenarios, node classes are imbalanced, with some majority classes making up most parts of the graph.

Node Classification

Prefix Conditioning Unifies Language and Label Supervision

no code implementations CVPR 2023 Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister

In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.

Classification Contrastive Learning +2

A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization

1 code implementation28 May 2022 Puyuan Liu, Xiang Zhang, Lili Mou

Sentence summarization aims at compressing a long sentence into a short one that keeps the main gist, and has extensive real-world applications such as headline generation.

Headline Generation Sentence Summarization

Towards Faithful and Consistent Explanations for Graph Neural Networks

no code implementations27 May 2022 Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

Two typical reasons of spurious explanations are identified: confounding effect of latent variables like distribution shift, and causal factors distinct from the original input.

Inductive Bias Network Interpretation

MMNet: A Model-Based Multimodal Network for Human Action Recognition in RGB-D Videos

1 code implementation IEEE Transactions on Pattern Analysis and Machine Intelligence 2022 Bruce X.B. Yu, Yan Liu, Xiang Zhang, Sheng-hua Zhong, Keith C.C. Chan

Upon aggregating the results of multiple modalities, our method is found to outperform state-of-the-art approaches on six evaluation protocols of the five datasets; thus, the proposed MMNet can effectively capture mutually complementary features in different RGB-D video modalities and provide more discriminative features for HAR.

 Ranked #1 on Action Recognition In Videos on PKU-MMD (using extra training data)

Action Classification Action Recognition In Videos +2

On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation

no code implementations12 Apr 2022 Wenju Zhang, Xiang Zhang, Qing Liao, Long Lan, Mengzhu Wang, Wei Wang, Baoyun Peng, Zhengming Ding

Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.

Image Classification Unsupervised Domain Adaptation

Text Spotting Transformers

1 code implementation CVPR 2022 Xiang Zhang, Yongwen Su, Subarna Tripathi, Zhuowen Tu

In this paper, we present TExt Spotting TRansformers (TESTR), a generic end-to-end text spotting framework using Transformers for text detection and recognition in the wild.

Text Detection Text Spotting

Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels

no code implementations30 Mar 2022 Xiaotian Li, Xiang Zhang, Taoyue Wang, Lijun Yin

Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations.

Out-of-Distribution Generalization

An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis

no code implementations29 Mar 2022 Xiaotian Li, Xiang Zhang, Huiyuan Yang, Wenna Duan, Weiying Dai, Lijun Yin

Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes.

Cultural Vocal Bursts Intensity Prediction EEG +2

Unifying Motion Deblurring and Frame Interpolation with Events

1 code implementation CVPR 2022 Xiang Zhang, Lei Yu

Slow shutter speed and long exposure time of frame-based cameras often cause visual blur and loss of inter-frame information, degenerating the overall quality of captured videos.

Deblurring Self-Supervised Learning +1

Multi-Modal Learning for AU Detection Based on Multi-Head Fused Transformers

no code implementations22 Mar 2022 Xiang Zhang, Lijun Yin

In this paper, we propose a novel end-to-end Multi-Head Fused Transformer (MFT) method for AU detection, which learns AU encoding features representation from different modalities by transformer encoder and fuses modalities by another fusion transformer module.

Action Unit Detection

Exploring Edge Disentanglement for Node Classification

no code implementations23 Feb 2022 Tianxiang Zhao, Xiang Zhang, Suhang Wang

Concretely, these self-supervision tasks are enforced on a designed edge disentanglement module to be trained jointly with the downstream node classification task to encourage automatic edge disentanglement.

Classification Disentanglement +2

AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models

no code implementations29 Jan 2022 Dongkuan Xu, Subhabrata Mukherjee, Xiaodong Liu, Debadeepta Dey, Wenhui Wang, Xiang Zhang, Ahmed Hassan Awadallah, Jianfeng Gao

Our framework AutoDistil addresses above challenges with the following steps: (a) Incorporates inductive bias and heuristics to partition Transformer search space into K compact sub-spaces (K=3 for typical student sizes of base, small and tiny); (b) Trains one SuperLM for each sub-space using task-agnostic objective (e. g., self-attention distillation) with weight-sharing of students; (c) Lightweight search for the optimal student without re-training.

Inductive Bias Knowledge Distillation +1

Synthetic Aperture Imaging With Events and Frames

1 code implementation CVPR 2022 Wei Liao, Xiang Zhang, Lei Yu, ShiJie Lin, Wen Yang, Ning Qiao

This paper addresses this problem by leveraging the merits of both events and frames, leading to a fusion-based SAI (EF-SAI) that performs consistently under the different densities of occlusions.

feature selection

InfoGCL: Information-Aware Graph Contrastive Learning

no code implementations NeurIPS 2021 Dongkuan Xu, Wei Cheng, Dongsheng Luo, Haifeng Chen, Xiang Zhang

The key point of this framework is to follow the Information Bottleneck principle to reduce the mutual information between contrastive parts while keeping task-relevant information intact at both the levels of the individual module and the entire framework so that the information loss during graph representation learning can be minimized.

Contrastive Learning Graph Classification +3

A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks

no code implementations17 Oct 2021 Xiang Zhang, Shamik Sarkar, Arupjyoti Bhuyan, Sneha Kumar Kasera, Mingyue Ji

The proposed approach can also be integrated into our previously developed Lyapunov stochastic optimization framework for the purpose of network utility maximization with optimality guarantee.

Management Q-Learning +2

Online Graph Learning in Dynamic Environments

no code implementations11 Oct 2021 Xiang Zhang

Inferring the underlying graph topology that characterizes structured data is pivotal to many graph-based models when pre-defined graphs are not available.

Graph Learning

Time-varying Graph Learning Under Structured Temporal Priors

no code implementations11 Oct 2021 Xiang Zhang, Qiao Wang

Different from many existing chain structure based methods in which the priors like temporal homogeneity can only describe the variations of two consecutive graphs, we propose a structure named \emph{temporal graph} to characterize the underlying real temporal relations.

Graph Learning

Graph-Guided Network for Irregularly Sampled Multivariate Time Series

2 code implementations ICLR 2022 Xiang Zhang, Marko Zeman, Theodoros Tsiligkaridis, Marinka Zitnik

Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data.

Time Series Time Series Analysis

Information-Aware Time Series Meta-Contrastive Learning

no code implementations29 Sep 2021 Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Haifeng Chen, Xiang Zhang

How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.

Contrastive Learning Meta-Learning +4

AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators

1 code implementation21 Sep 2021 Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang

Most of the existing contrastive learning methods employ pre-defined view generation methods, e. g., node drop or edge perturbation, which usually cannot adapt to input data or preserve the original semantic structures well.

Contrastive Learning Graph Representation Learning +3

ECG-Based Heart Arrhythmia Diagnosis Through Attentional Convolutional Neural Networks

1 code implementation18 Aug 2021 Ziyu Liu, Xiang Zhang

Electrocardiography (ECG) signal is a highly applied measurement for individual heart condition, and much effort have been endeavored towards automatic heart arrhythmia diagnosis based on machine learning.

Arrhythmia Detection BIG-bench Machine Learning

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface

no code implementations27 Jul 2021 Shuhang Chen, Xiang Zhang, Xiang Shen, Yifan Huang, Yiwen Wang

In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework.

Using Terminal Circuit for Power System Electromagnetic Transient Simulation

no code implementations1 Jul 2021 Yijing Liu, Xiang Zhang, Renchang Dai, Guangyi Liu

The modern power system is evolving with increasing penetration of power electronics introducing complicated electromagnetic phenomenon.

byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings

no code implementations24 Jun 2021 Xiang Zhang, Alexandre Drouin, Raymond Li

This article introduces byteSteady -- a fast model for classification using byte-level n-gram embeddings.

text-classification Text Classification

Controlling Neural Networks with Rule Representations

1 code implementation NeurIPS 2021 Sungyong Seo, Sercan O. Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister

The key aspect of DeepCTRL is that it does not require retraining to adapt the rule strength -- at inference, the user can adjust it based on the desired operation point on accuracy vs. rule verification ratio.

Decision Making

Power System Transient Modeling and Simulation using Integrated Circuit

no code implementations6 Jun 2021 Xiang Zhang, Renchang Dai, Peng Wei, Yijing Liu, Guangyi Liu, Zhiwei Wang

Transient stability analysis (TSA) plays an important role in power system analysis to investigate the stability of power system.

Numerical Integration

Robust Graph Learning Under Wasserstein Uncertainty

no code implementations10 May 2021 Xiang Zhang, Yinfei Xu, Qinghe Liu, Zhicheng Liu, Jian Lu, Qiao Wang

To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data.

Graph Learning

BM-NAS: Bilevel Multimodal Neural Architecture Search

1 code implementation19 Apr 2021 Yihang Yin, Siyu Huang, Xiang Zhang

Deep neural networks (DNNs) have shown superior performances on various multimodal learning problems.

Neural Architecture Search

Pose Recognition with Cascade Transformers

2 code implementations CVPR 2021 Ke Li, Shijie Wang, Xiang Zhang, Yifan Xu, Weijian Xu, Zhuowen Tu

Here we utilize the encoder-decoder structure in Transformers to perform regression-based person and keypoint detection that is general-purpose and requires less heuristic design compared with the existing approaches.

Keypoint Detection regression

Unsupervised Document Embedding via Contrastive Augmentation

1 code implementation26 Mar 2021 Dongsheng Luo, Wei Cheng, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Bo Zong, Yanchi Liu, Zhengzhang Chen, Dongjin Song, Haifeng Chen, Xiang Zhang

We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.

Contrastive Learning Data Augmentation +3

GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

2 code implementations16 Mar 2021 Tianxiang Zhao, Xiang Zhang, Suhang Wang

This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs.

Classification General Classification +2

Semi-Supervised Graph-to-Graph Translation

no code implementations16 Mar 2021 Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, Suhang Wang

For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain.

Graph-To-Graph Translation Translation

Event-based Synthetic Aperture Imaging with a Hybrid Network

1 code implementation CVPR 2021 Xiang Zhang, Wei Liao, Lei Yu, Wen Yang, Gui-Song Xia

Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images.

Style Transfer

Uncoordinated Spectrum Sharing in Millimeter Wave Networks Using Carrier Sensing

no code implementations24 Feb 2021 Shamik Sarkar, Xiang Zhang, Arupjyoti Bhuyan, Mingyue Ji, Sneha Kumar Kasera

Using stochastic geometry, we develop a general framework for downlink coverage probability analysis of our shared mmWave network in the presence of CS and derive the downlink coverage probability expressions for several CS protocols.

Information Theory Networking and Internet Architecture Information Theory

A New Design of Cache-aided Multiuser Private Information Retrieval with Uncoded Prefetching

no code implementations2 Feb 2021 Xiang Zhang, Kai Wan, Hua Sun, Mingyue Ji, Giuseppe Caire

This paper considers the MuPIR problem with two messages, arbitrary number of users and databases where uncoded prefetching is assumed, i. e., the users directly copy some bits from the library as their cached contents.

Information Retrieval Retrieval

A Two-stream Neural Network for Pose-based Hand Gesture Recognition

no code implementations22 Jan 2021 Chuankun Li, Shuai Li, Yanbo Gao, Xiang Zhang, Wanqing Li

The self-attention based graph convolutional network has a dynamic self-attention mechanism to adaptively exploit the relationships of all hand joints in addition to the fixed topology and local feature extraction in the GCN.

Action Recognition Hand Gesture Recognition +2

DyHCN: Dynamic Hypergraph Convolutional Networks

no code implementations1 Jan 2021 Nan Yin, Zhigang Luo, Wenjie Wang, Fuli Feng, Xiang Zhang

In general, DyHCN consists of a Hypergraph Convolution (HC) to encode the hypergraph structure at a time point and a Temporal Evolution module (TE) to capture the varying of the relations.

Improving Unsupervised Domain Adaptation by Reducing Bi-level Feature Redundancy

no code implementations28 Dec 2020 Mengzhu Wang, Xiang Zhang, Long Lan, Wei Wang, Huibin Tan, Zhigang Luo

In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way.

Unsupervised Domain Adaptation

Parameterized Explainer for Graph Neural Network

3 code implementations NeurIPS 2020 Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang

The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.

Graph Classification

Attentive Social Recommendation: Towards User And Item Diversities

1 code implementation9 Nov 2020 Dongsheng Luo, Yuchen Bian, Xiang Zhang, Jun Huan

Social recommendation system is to predict unobserved user-item rating values by taking advantage of user-user social relation and user-item ratings.

On the Fundamental Limits of Cache-aided Multiuser Private Information Retrieval

no code implementations13 Oct 2020 Xiang Zhang, Kai Wan, Hua Sun, Mingyue Ji, Giuseppe Caire

Based on the proposed novel approach of \emph{cache-aided interference alignment (CIA)}, first, for the MuPIR problem with $K=2$ messages, $K_{\rm u}=2$ users and $N\ge 2$ databases, we propose achievable retrieval schemes for both uncoded and general cache placement.

Information Retrieval Retrieval

Improving Spiking Sparse Recovery via Non-Convex Penalties

no code implementations19 Sep 2020 Xiang Zhang, Lei Yu, Gang Zheng

Compared with digital methods, sparse recovery based on spiking neural networks has great advantages like high computational efficiency and low power-consumption.

Automatic Image Labelling at Pixel Level

no code implementations15 Jul 2020 Xiang Zhang, Wei zhang, Jinye Peng, Jianping Fan

A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from a source domain, and such GFN then transfers such segmentation knowledge to generate coarse object masks in the target domain.

Image Segmentation Segmentation +1

GNNGuard: Defending Graph Neural Networks against Adversarial Attacks

1 code implementation NeurIPS 2020 Xiang Zhang, Marinka Zitnik

Here, we develop GNNGuard, a general algorithm to defend against a variety of training-time attacks that perturb the discrete graph structure.

Literature Triage on Genomic Variation Publications by Knowledge-enhanced Multi-channel CNN

no code implementations8 May 2020 Chenhui Lv, Qian Lu, Xiang Zhang

Conclusions: Multiple channels and knowledge embeddings enhance the performance of the CNN model in the task of biomedical literature triage.

BIG-bench Machine Learning

ProbaNet: Proposal-balanced Network for Object Detection

no code implementations6 May 2020 Jing Wu, Xiang Zhang, Mingyi Zhou, Ce Zhu

Candidate object proposals generated by object detectors based on convolutional neural network (CNN) encounter easy-hard samples imbalance problem, which can affect overall performance.

object-detection Object Detection

Adversarial Imitation Attack

no code implementations28 Mar 2020 Mingyi Zhou, Jing Wu, Yipeng Liu, Xiaolin Huang, Shuaicheng Liu, Xiang Zhang, Ce Zhu

Then, the adversarial examples generated by the imitation model are utilized to fool the attacked model.

Adversarial Attack

Entity Profiling in Knowledge Graphs

no code implementations29 Feb 2020 Xiang Zhang, Qingqing Yang, Jinru Ding, Ziyue Wang

Traditional profiling technologies encompass a vast array of methods to find distinctive features in various applications, which can help to differentiate entities in the process of human understanding of KGs.

Knowledge Graphs Representation Learning

A Multi-view CNN-based Acoustic Classification System for Automatic Animal Species Identification

no code implementations23 Feb 2020 Weitao Xu, Xiang Zhang, Lina Yao, Wanli Xue, Bo Wei

In this paper, we propose a deep learning based acoustic classification framework for Wireless Acoustic Sensor Network (WASN).

Classification feature selection +1

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

1 code implementation18 Sep 2019 Xiang Zhang, Lina Yao, Manqing Dong, Zhe Liu, Yu Zhang, Yong Li

Furthermore, to enhance the explainability, we develop an attention mechanism to automatically learn the importance of each EEG channels in the seizure diagnosis procedure.

EEG Electroencephalogram (EEG) +3

Multi-task Generative Adversarial Learning on Geometrical Shape Reconstruction from EEG Brain Signals

2 code implementations31 Jul 2019 Xiang Zhang, Xiaocong Chen, Manqing Dong, Huan Liu, Chang Ge, Lina Yao

In light of this, we propose a novel multi-task generative adversarial network to convert the individual's EEG signals evoked by geometrical shapes to the original geometry.

EEG Electroencephalogram (EEG) +1

Deep Neural Network Hyperparameter Optimization with Orthogonal Array Tuning

1 code implementation31 Jul 2019 Xiang Zhang, Xiaocong Chen, Lina Yao, Chang Ge, Manqing Dong

Deep learning algorithms have achieved excellent performance lately in a wide range of fields (e. g., computer version).

Bayesian Optimization Hyperparameter Optimization

Residual-Guided In-Loop Filter Using Convolution Neural Network

no code implementations29 Jul 2019 Wei Jia, Li Li, Zhu Li, Xiang Zhang, Shan Liu

The block-based coding structure in the hybrid video coding framework inevitably introduces compression artifacts such as blocking, ringing, etc.


BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis

no code implementations13 Jul 2019 Yu Gu, Xiang Zhang, Zhi Liu, Fuji Ren

The ever evolving informatics technology has gradually bounded human and computer in a compact way.

AdaNSP: Uncertainty-driven Adaptive Decoding in Neural Semantic Parsing

no code implementations ACL 2019 Xiang Zhang, Shizhu He, Kang Liu, Jun Zhao

To keep the model aware of the underlying grammar in target sequences, many constrained decoders were devised in a multi-stage paradigm, which decode to the sketches or abstract syntax trees first, and then decode to target semantic tokens.

Semantic Parsing

A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers

no code implementations10 May 2019 Xiang Zhang, Lina Yao, Xianzhi Wang, Jessica Monaghan, David Mcalpine, Yu Zhang

Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.

Adversarial Variational Embedding for Robust Semi-supervised Learning

1 code implementation7 May 2019 Xiang Zhang, Lina Yao, Feng Yuan

However, the latent code learned by the traditional VAE is not exclusive (repeatable) for a specific input sample, which prevents it from excellent classification performance.

General Classification

Exploiting Coarse-to-Fine Task Transfer for Aspect-level Sentiment Classification

1 code implementation AAAI 2019 2018 Zheng Li, Ying WEI, Yu Zhang, Xiang Zhang, Xin Li, Qiang Yang

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT).

General Classification Sentiment Analysis +1

Adversarially-Trained Normalized Noisy-Feature Auto-Encoder for Text Generation

no code implementations10 Nov 2018 Xiang Zhang, Yann Lecun

An ATNNFAE consists of an auto-encoder where the internal code is normalized on the unit sphere and corrupted by additive noise.

Text Generation

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Multi-modality Sensor Data Classification with Selective Attention

no code implementations16 Apr 2018 Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, Can Wang

Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.

Classification General Classification

A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification

no code implementations2 Apr 2018 Weicheng Cai, Zexin Cai, Xiang Zhang, Xiaoqi Wang, Ming Li

A novel learnable dictionary encoding layer is proposed in this paper for end-to-end language identification.

Language Identification

Metric Factorization: Recommendation beyond Matrix Factorization

2 code implementations13 Feb 2018 Shuai Zhang, Lina Yao, Yi Tay, Xiwei Xu, Xiang Zhang, Liming Zhu

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.

Byte-Level Recursive Convolutional Auto-Encoder for Text

1 code implementation ICLR 2018 Xiang Zhang, Yann Lecun

The proposed model is a multi-stage deep convolutional encoder-decoder framework using residual connections, containing up to 160 parameterized layers.

Text Generation

MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network

2 code implementations16 Nov 2017 Xiang Zhang, Lina Yao, Salil S. Kanhere, Yunhao Liu, Tao Gu, Kai-Xuan Chen

The proposed approach is evaluated over 3 datasets (two local and one public).

Human-Computer Interaction

Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals

2 code implementations26 Sep 2017 Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu, Dalin Zhang

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables people to communicate with the outside world by interpreting the EEG signals of their brains to interact with devices such as wheelchairs and intelligent robots.

EEG Electroencephalogram (EEG) +1

Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis

no code implementations26 Sep 2017 Xiang Zhang, Lina Yao, Dalin Zhang, Xianzhi Wang, Quan Z. Sheng, Tao Gu

In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition.

Activity Recognition EEG +2

Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface

no code implementations22 Aug 2017 Dalin Zhang, Lina Yao, Xiang Zhang, Sen Wang, Weitong Chen, Robert Boots

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions.

Human-Computer Interaction Neurons and Cognition

Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?

3 code implementations8 Aug 2017 Xiang Zhang, Yann Lecun

This article offers an empirical study on the different ways of encoding Chinese, Japanese, Korean (CJK) and English languages for text classification.

General Classification Text Classification

Learning to Predict Charges for Criminal Cases with Legal Basis

no code implementations EMNLP 2017 Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, Dongyan Zhao

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description.

DeepKey: An EEG and Gait Based Dual-Authentication System

no code implementations6 Jun 2017 Xiang Zhang, Lina Yao, Chaoran Huang, Tao Gu, Zheng Yang, Yunhao Liu

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics.

EEG Electroencephalogram (EEG) +2

Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

no code implementations17 Aug 2016 Xiang Zhang, Jiarui Sun, Siwei Ma, Zhouchen Lin, Jian Zhang, Shiqi Wang, Wen Gao

Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation.

Data Compression Dictionary Learning

Universum Prescription: Regularization using Unlabeled Data

no code implementations11 Nov 2015 Xiang Zhang, Yann Lecun

This paper shows that simply prescribing "none of the above" labels to unlabeled data has a beneficial regularization effect to supervised learning.

Image Classification

Text Understanding from Scratch

3 code implementations5 Feb 2015 Xiang Zhang, Yann Lecun

This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets).

General Classification Sentiment Analysis

OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks

4 code implementations21 Dec 2013 Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus, Yann Lecun

This integrated framework is the winner of the localization task of the ImageNet Large Scale Visual Recognition Challenge 2013 (ILSVRC2013) and obtained very competitive results for the detection and classifications tasks.

General Classification Image Classification +2

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