Search Results for author: Wei Cheng

Found 43 papers, 17 papers with code

Time Series Contrastive Learning with Information-Aware Augmentations

no code implementations21 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 Representation Learning

Dynamic Prompting: A Unified Framework for Prompt Tuning

no code implementations6 Mar 2023 Xianjun Yang, Wei Cheng, Xujiang Zhao, Linda Petzold, Haifeng Chen

In particular, the inserted prompt position, length, and the representations of prompts for diversified instances through different tasks could all affect the prompt tuning performance.

Exploring the Limits of ChatGPT for Query or Aspect-based Text Summarization

no code implementations16 Feb 2023 Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, Wei Cheng

Text summarization has been a crucial problem in natural language processing (NLP) for several decades.

Abstractive Text Summarization

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

Deep Federated Anomaly Detection for Multivariate Time Series Data

no code implementations9 May 2022 Wei Zhu, Dongjin Song, Yuncong Chen, Wei Cheng, Bo Zong, Takehiko Mizoguchi, Cristian Lumezanu, Haifeng Chen, Jiebo Luo

Specifically, we first design an Exemplar-based Deep Neural network (ExDNN) to learn local time series representations based on their compatibility with an exemplar module which consists of hidden parameters learned to capture varieties of normal patterns on each edge device.

Federated Learning Time Series Analysis +1

Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis

1 code implementation25 Apr 2022 Wei Cheng, Su Xu, Jingtan Piao, Chen Qian, Wayne Wu, Kwan-Yee Lin, Hongsheng Li

Specifically, we compress the light fields for novel view human rendering as conditional implicit neural radiance fields from both geometry and appearance aspects.

Novel View Synthesis

SEED: Sound Event Early Detection via Evidential Uncertainty

no code implementations5 Feb 2022 Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen

Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.

Event Detection Sound Event Detection

Do Multi-Lingual Pre-trained Language Models Reveal Consistent Token Attributions in Different Languages?

no code implementations23 Dec 2021 Junxiang Wang, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao

During the past several years, a surge of multi-lingual Pre-trained Language Models (PLMs) has been proposed to achieve state-of-the-art performance in many cross-lingual downstream tasks.

Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph

1 code implementation1 Dec 2021 Liyan Xu, Xuchao Zhang, Bo Zong, Yanchi Liu, Wei Cheng, Jingchao Ni, Haifeng Chen, Liang Zhao, Jinho D. Choi

We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence.

Machine Reading Comprehension

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 +2

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 +2

Code Editing from Few Exemplars by Adaptive Multi-Extent Composition

no code implementations29 Sep 2021 Peizhao Li, Xuchao Zhang, Ziyu Yao, Wei Cheng, Haifeng Chen, Hongfu Liu

To achieve this, we propose a machine learning approach to adapt the editorial style derived from few exemplars to a query code snippet.

Superclass-Conditional Gaussian Mixture Model For Learning Fine-Grained Embeddings

1 code implementation ICLR 2022 Jingchao Ni, Wei Cheng, Zhengzhang Chen, Takayoshi Asakura, Tomoya Soma, Sho Kato, Haifeng Chen

The dilemma necessitates the adaptation of a "coarsely" pretrained model to new tasks with a few unseen "finer-grained" training labels.

Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extraction

no code implementations Findings (EMNLP) 2021 Zeyu Li, Wei Cheng, Reema Kshetramade, John Houser, Haifeng Chen, Wei Wang

Compliments and concerns in reviews are valuable for understanding users' shopping interests and their opinions with respect to specific aspects of certain items.

Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media

no code implementations8 Jun 2021 Tao Na, Wei Cheng, Dongming Li, Wanyu Lu, Hongjiang Li

We found residents in the two countries are willing to share their views and feelings concerning the vaccine.

Sentiment Analysis

FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems

1 code implementation CVPR 2021 Liang Tong, Zhengzhang Chen, Jingchao Ni, Wei Cheng, Dongjin Song, Haifeng Chen, Yevgeniy Vorobeychik

Moreover, we observe that open-set face recognition systems are more vulnerable than closed-set systems under different types of attacks.

Face Recognition

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

Local strict singular characteristics: Cauchy problem with smooth initial data

no code implementations10 Mar 2021 Wei Cheng, Jiahui Hong

Especially, we obtain an existence result of smooth strict singular characteristic from and to non-conjugate singular initial point based on the structure of the superdifferential of the solution, which is even new in the classical time-dependent case.

Analysis of PDEs

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

1 code implementation3 Mar 2021 Yinjun Wu, Jingchao Ni, Wei Cheng, Bo Zong, Dongjin Song, Zhengzhang Chen, Yanchi Liu, Xuchao Zhang, Haifeng Chen, Susan Davidson

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications.

Time Series Analysis

Cumulant Expansion of Mutual Information for Quantifying Leakage of a Protected Secret

no code implementations4 Feb 2021 Olivier Rioul, Wei Cheng, Sylvain Guilley

The information leakage of a cryptographic implementation with a given degree of protection is evaluated in a typical situation when the signal-to-noise ratio is small.

Information Theory Information Theory

Aspect-based Sentiment Classification via Reinforcement Learning

no code implementations1 Jan 2021 Lichen Wang, Bo Zong, Yunyu Liu, Can Qin, Wei Cheng, Wenchao Yu, Xuchao Zhang, Haifeng Chen, Yun Fu

As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step.

Classification General Classification +4

SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression

1 code implementation15 Dec 2020 Wei Cheng, Ghulam Murtaza, Aaron Wang

The emergence of large-scale data sets provides great opportunities for better understanding of genomics, especially gene regulation.

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

T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting

no code implementations26 Oct 2020 Denghui Zhang, Yanchi Liu, Wei Cheng, Bo Zong, Jingchao Ni, Zhengzhang Chen, Haifeng Chen, Hui Xiong

Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.

Generalizing Variational Autoencoders with Hierarchical Empirical Bayes

1 code implementation20 Jul 2020 Wei Cheng, Gregory Darnell, Sohini Ramachandran, Lorin Crawford

Recent methods have mitigated this issue by deterministically moment-matching an aggregated posterior distribution to an aggregate prior.

Inductive and Unsupervised Representation Learning on Graph Structured Objects

no code implementations ICLR 2020 Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu

Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.

Graph Learning Graph Similarity +3

Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

no code implementations18 Dec 2019 Xin Dong, Jingchao Ni, Wei Cheng, Zhengzhang Chen, Bo Zong, Dongjin Song, Yanchi Liu, Haifeng Chen, Gerard de Melo

In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.

Recommendation Systems

Semantic Graph Convolutional Network for Implicit Discourse Relation Classification

no code implementations21 Oct 2019 Yingxue Zhang, Ping Jian, Fandong Meng, Ruiying Geng, Wei Cheng, Jie zhou

Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives communicating these relations.

Classification Discourse Parsing +2

Learning Robust Representations with Graph Denoising Policy Network

no code implementations4 Oct 2019 Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Wei zhang, Hongyuan Zha, Xiaofeng He, Haifeng Chen

Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.

Denoising Graph Representation Learning +2

A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

5 code implementations20 Nov 2018 Chuxu Zhang, Dongjin Song, Yuncong Chen, Xinyang Feng, Cristian Lumezanu, Wei Cheng, Jingchao Ni, Bo Zong, Haifeng Chen, Nitesh V. Chawla

Subsequently, given the signature matrices, a convolutional encoder is employed to encode the inter-sensor (time series) correlations and an attention based Convolutional Long-Short Term Memory (ConvLSTM) network is developed to capture the temporal patterns.

Time Series Anomaly Detection Unsupervised Anomaly Detection

Target Transfer Q-Learning and Its Convergence Analysis

no code implementations21 Sep 2018 Yue Wang, Qi Meng, Wei Cheng, Yuting Liug, Zhi-Ming Ma, Tie-Yan Liu

In this paper, we propose to transfer the Q-function learned in the source task to the target of the Q-learning in the new task when certain safe conditions are satisfied.

Q-Learning Reinforcement Learning (RL) +1

Learning Deep Network Representations with Adversarially Regularized Autoencoders

1 code implementation ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang

The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.

Link Prediction Multi-Label Classification +1

FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification

no code implementations25 Apr 2017 Haoyi Xiong, Wei Cheng, Wenqing Hu, Jiang Bian, Zhishan Guo

Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e. g., covariance matrix), and the "linear inseparability" of EHR data.

Classification General Classification

A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

14 code implementations7 Apr 2017 Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison Cottrell

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades.

Time Series Prediction

FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras

no code implementations29 Oct 2016 Lan Xu, Lu Fang, Wei Cheng, Kaiwen Guo, Guyue Zhou, Qionghai Dai, Yebin Liu

We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera.

Markerless Motion Capture Visual Odometry

Particle Swarm Optimized Power Consumption of Trilateration

no code implementations8 Feb 2016 Hussein S. Al-Olimat, Robert C. Green II, Mansoor Alam, Vijay Devabhaktuni, Wei Cheng

Trilateration-based localization (TBL) has become a corner stone of modern technology.

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