Search Results for author: Chao Wu

Found 54 papers, 19 papers with code

Constructing Data Transaction Chains Based on Opportunity Cost Exploration

no code implementations8 Apr 2024 Jie Liu, Tao Feng, Yan Jiang, Peizheng Wang, Chao Wu

However, the inherent replicability and privacy concerns of data make it challenging to directly apply traditional trading theories to data markets.

Efficient Pruning of Large Language Model with Adaptive Estimation Fusion

no code implementations16 Mar 2024 Jun Liu, Chao Wu, Changdi Yang, Hao Tang, Haoye Dong, Zhenglun Kong, Geng Yuan, Wei Niu, Dong Huang, Yanzhi Wang

Large language models (LLMs) have become crucial for many generative downstream tasks, leading to an inevitable trend and significant challenge to deploy them efficiently on resource-constrained devices.

Language Modelling Large Language Model

From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations

1 code implementation15 Mar 2024 Xinli Hao, Yile Chen, Chen Yang, Zhihui Du, Chaohong Ma, Chao Wu, Xiaofeng Meng

However, existing time series anomaly detection methods fall short in tackling the unique characteristics of astronomical observations where each star is inherently independent but interfered by random concurrent noise, resulting in a high rate of false alarms.

Graph structure learning Time Series +2

Distributionally Generative Augmentation for Fair Facial Attribute Classification

1 code implementation11 Mar 2024 Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang

This work proposes a novel, generation-based two-stage framework to train a fair FAC model on biased data without additional annotation.

Attribute Classification +2

Improving Group Connectivity for Generalization of Federated Deep Learning

no code implementations29 Feb 2024 Zexi Li, Jie Lin, Zhiqi Li, Didi Zhu, Chao Wu

Bridging the gap between LMC and FL, in this paper, we leverage fixed anchor models to empirically and theoretically study the transitivity property of connectivity from two models (LMC) to a group of models (model fusion in FL).

Federated Learning Linear Mode Connectivity

Generalizable Two-Branch Framework for Image Class-Incremental Learning

no code implementations28 Feb 2024 Chao Wu, Xiaobin Chang, Ruixuan Wang

Specifically, the main branch can be any existing CL model and the newly introduced side branch is a lightweight convolutional network.

Class Incremental Learning Incremental Learning

Model Tailor: Mitigating Catastrophic Forgetting in Multi-modal Large Language Models

no code implementations19 Feb 2024 Didi Zhu, Zhongyi Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Kun Kuang, Chao Wu

Catastrophic forgetting emerges as a critical challenge when fine-tuning multi-modal large language models (MLLMs), where improving performance on unseen tasks often leads to a significant performance drop on the original tasks.

Image Captioning Question Answering +1

Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusion

no code implementations2 Feb 2024 Zexi Li, Zhiqi Li, Jie Lin, Tao Shen, Tao Lin, Chao Wu

In deep learning, stochastic gradient descent often yields functionally similar yet widely scattered solutions in the weight space even under the same initialization, causing barriers in the Linear Mode Connectivity (LMC) landscape.

Federated Learning Linear Mode Connectivity

EdgeOL: Efficient in-situ Online Learning on Edge Devices

no code implementations30 Jan 2024 Sheng Li, Geng Yuan, Yawen Wu, Yue Dai, Chao Wu, Alex K. Jones, Jingtong Hu, Yanzhi Wang, Xulong Tang

Emerging applications, such as robot-assisted eldercare and object recognition, generally employ deep learning neural networks (DNNs) and naturally require: i) handling streaming-in inference requests and ii) adapting to possible deployment scenario changes.

Object Recognition

Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers

1 code implementation19 Dec 2023 Ruiyuan Zhang, Jiaxiang Liu, Zexi Li, Hao Dong, Jie Fu, Chao Wu

Therefore, there is a need to develop a scalable framework for geometric fracture assembly without relying on semantic information.

3D Assembly

Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge

no code implementations9 Dec 2023 Xuan Shen, Peiyan Dong, Lei Lu, Zhenglun Kong, Zhengang Li, Ming Lin, Chao Wu, Yanzhi Wang

Recent works show that 8-bit or lower weight quantization is feasible with minimal impact on end-to-end task performance, while the activation is still not quantized.

Language Modelling Quantization

FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning

no code implementations30 Nov 2023 Lingzhi Gao, Zexi Li, Yang Lu, Chao Wu

A typical way of pFL focuses on label distribution skew, and they adopt a decoupling scheme where the model is split into a common feature extractor and two prediction heads (generic and personalized).

Personalized Federated Learning

Score-PA: Score-based 3D Part Assembly

1 code implementation8 Sep 2023 Junfeng Cheng, Mingdong Wu, Ruiyuan Zhang, Guanqi Zhan, Chao Wu, Hao Dong

In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly.

Using Adamic-Adar Index Algorithm to Predict Volunteer Collaboration: Less is More

no code implementations25 Aug 2023 Chao Wu, Peng Chen, Baiqiao Yin, Zijuan Lin, Chen Jiang, Di Yu, Changhong Zou, Chunwang Lui

Social networks exhibit a complex graph-like structure due to the uncertainty surrounding potential collaborations among participants.

Ensemble Learning Link Prediction

Understanding Prompt Tuning for V-L Models Through the Lens of Neural Collapse

no code implementations28 Jun 2023 Didi Zhu, Zexi Li, Min Zhang, Junkun Yuan, Yunfeng Shao, Jiashuo Liu, Kun Kuang, Yinchuan Li, Chao Wu

It is found that NC optimality of text-to-image representations shows a positive correlation with downstream generalizability, which is more severe under class imbalance settings.

Generalized Universal Domain Adaptation with Generative Flow Networks

no code implementations8 May 2023 Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao, Fei Wu, Kun Kuang, Jun Xiao, Chao Wu

We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories.

Universal Domain Adaptation Unsupervised Domain Adaptation

Data valuation: The partial ordinal Shapley value for machine learning

1 code implementation2 May 2023 Jie Liu, Peizheng Wang, Chao Wu

Data valuation using Shapley value has emerged as a prevalent research domain in machine learning applications.

Abstract Algebra Data Valuation

Universal Domain Adaptation via Compressive Attention Matching

no code implementations ICCV 2023 Didi Zhu, Yincuan Li, Junkun Yuan, Zexi Li, Kun Kuang, Chao Wu

To address this issue, we propose a Universal Attention Matching (UniAM) framework by exploiting the self-attention mechanism in vision transformer to capture the crucial object information.

Universal Domain Adaptation

No Fear of Classifier Biases: Neural Collapse Inspired Federated Learning with Synthetic and Fixed Classifier

1 code implementation ICCV 2023 Zexi Li, Xinyi Shang, Rui He, Tao Lin, Chao Wu

Recent advances in neural collapse have shown that the classifiers and feature prototypes under perfect training scenarios collapse into an optimal structure called simplex equiangular tight frame (ETF).

Classifier calibration Federated Learning

Delving into the Adversarial Robustness of Federated Learning

no code implementations19 Feb 2023 Jie Zhang, Bo Li, Chen Chen, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chao Wu

In this work, we propose a novel algorithm called Decision Boundary based Federated Adversarial Training (DBFAT), which consists of two components (local re-weighting and global regularization) to improve both accuracy and robustness of FL systems.

Adversarial Robustness Federated Learning

Revisiting Weighted Aggregation in Federated Learning with Neural Networks

1 code implementation14 Feb 2023 Zexi Li, Tao Lin, Xinyi Shang, Chao Wu

In federated learning (FL), weighted aggregation of local models is conducted to generate a global model, and the aggregation weights are normalized (the sum of weights is 1) and proportional to the local data sizes.

Federated Learning

All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management

no code implementations9 Dec 2022 Yifan Gong, Zheng Zhan, Pu Zhao, Yushu Wu, Chao Wu, Caiwen Ding, Weiwen Jiang, Minghai Qin, Yanzhi Wang

By re-configuring the model to the corresponding pruning ratio for a specific execution frequency (and voltage), we are able to achieve stable inference speed, i. e., keeping the difference in speed performance under various execution frequencies as small as possible.


Federated Learning with Label Distribution Skew via Logits Calibration

2 code implementations1 Sep 2022 Jie Zhang, Zhiqi Li, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Chao Wu

Extensive experiments on federated datasets and real-world datasets demonstrate that FedLC leads to a more accurate global model and much improved performance.

Federated Learning

S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

no code implementations20 Jun 2022 Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu

To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.

Multi-agent Reinforcement Learning Reinforcement Learning (RL) +2

Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

1 code implementation CVPR 2022 Chao Wu, Wenhang Ge, AnCong Wu, Xiaobin Chang

To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role.

Person Re-Identification

Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching

no code implementations23 Mar 2022 Zexi Li, Jiaxun Lu, Shuang Luo, Didi Zhu, Yunfeng Shao, Yinchuan Li, Zhimeng Zhang, Yongheng Wang, Chao Wu

In the literature, centralized clustered FL algorithms require the assumption of the number of clusters and hence are not effective enough to explore the latent relationships among clients.

Federated Learning

Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning

1 code implementation28 Jan 2022 Jie Zhang, Lei Zhang, Gang Li, Chao Wu

Adversarial examples are inputs for machine learning models that have been designed by attackers to cause the model to make mistakes.

Towards Efficient Data Free Black-Box Adversarial Attack

1 code implementation CVPR 2022 Jie Zhang, Bo Li, Jianghe Xu, Shuang Wu, Shouhong Ding, Lei Zhang, Chao Wu

The proposed method can efficiently imitate the target model through a small number of queries and achieve high attack success rate.

Adversarial Attack

DENSE: Data-Free One-Shot Federated Learning

1 code implementation23 Dec 2021 Jie Zhang, Chen Chen, Bo Li, Lingjuan Lyu, Shuang Wu, Shouhong Ding, Chunhua Shen, Chao Wu

One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round.

Federated Learning

Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI

1 code implementation11 Nov 2021 Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang

However, edge computing, especially edge and cloud collaborative computing, are still in its infancy to announce their success due to the resource-constrained IoT scenarios with very limited algorithms deployed.

Cloud Computing Edge-computing

Unified Group Fairness on Federated Learning

no code implementations9 Nov 2021 Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao

We validate the advantages of the FMDA-M algorithm with various kinds of distribution shift settings in experiments, and the results show that FMDA-M algorithm outperforms the existing fair FL algorithms on unified group fairness.

Attribute Fairness +1

Ensemble Federated Adversarial Training with Non-IID data

no code implementations26 Oct 2021 Shuang Luo, Didi Zhu, Zexi Li, Chao Wu

Despite federated learning endows distributed clients with a cooperative training mode under the premise of protecting data privacy and security, the clients are still vulnerable when encountering adversarial samples due to the lack of robustness.

Federated Learning

Federated Graph Learning -- A Position Paper

no code implementations24 May 2021 Huanding Zhang, Tao Shen, Fei Wu, Mingyang Yin, Hongxia Yang, Chao Wu

Federated learning (FL) is a an emerging technique that can collaboratively train a shared model while keeping the data decentralized, which is a rational solution for distributed GNN training.

Federated Learning Graph Learning +1

Efficient Ring-topology Decentralized Federated Learning with Deep Generative Models for Industrial Artificial Intelligent

no code implementations15 Apr 2021 Zhao Wang, Yifan Hu, Jun Xiao, Chao Wu

A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization.

Federated Learning

Verifying Design through Generative Visualization of Neural Activities

no code implementations28 Mar 2021 Pan Wang, Danlin Peng, Simiao Yu, Chao Wu, Peter Childs, Yike Guo, Ling Li

A recurrent neural network is used as the encoder to learn latent representation from electroencephalogram (EEG) signals, recorded while subjects looked at 50 categories of images.

EEG Generative Adversarial Network

Multiple-element joint detection for Aspect-Based Sentiment Analysis

no code implementations Knowledge Based Systems 2020 Chao Wu, Qingyu Xiong, Hualing Yi, Yang Yu, Qiwu Zhu, Min Gao, Jie Chen

In this paper, we propose a novel end-to-end multiple-element joint detection model (MEJD), which effectively extracts all (target, aspect, sentiment) triples from a sentence.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

GFL: A Decentralized Federated Learning Framework Based On Blockchain

no code implementations21 Oct 2020 Yifan Hu, YuHang Zhou, Jun Xiao, Chao Wu

Federated learning(FL) is a rapidly growing field and many centralized and decentralized FL frameworks have been proposed.

Data Poisoning Federated Learning

Federated Unsupervised Representation Learning

no code implementations18 Oct 2020 Fengda Zhang, Kun Kuang, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Yueting Zhuang, Xiaolin Li

FURL poses two new challenges: (1) data distribution shift (Non-IID distribution) among clients would make local models focus on different categories, leading to the inconsistency of representation spaces.

Federated Learning Representation Learning

Federated Mutual Learning

3 code implementations27 Jun 2020 Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Kun Kuang, Fei Wu, Chao Wu

The experiments show that FML can achieve better performance than alternatives in typical FL setting, and clients can be benefited from FML with different models and tasks.

Federated Learning

Evaluation Framework For Large-scale Federated Learning

1 code implementation3 Mar 2020 Lifeng Liu, Fengda Zhang, Jun Xiao, Chao Wu

Federated learning is proposed as a machine learning setting to enable distributed edge devices, such as mobile phones, to collaboratively learn a shared prediction model while keeping all the training data on device, which can not only take full advantage of data distributed across millions of nodes to train a good model but also protect data privacy.

Federated Learning

Transfer Heterogeneous Knowledge Among Peer-to-Peer Teammates: A Model Distillation Approach

no code implementations6 Feb 2020 Zeyue Xue, Shuang Luo, Chao Wu, Pan Zhou, Kaigui Bian, Wei Du

Peer-to-peer knowledge transfer in distributed environments has emerged as a promising method since it could accelerate learning and improve team-wide performance without relying on pre-trained teachers in deep reinforcement learning.

Transfer Learning

Galaxy Learning -- A Position Paper

no code implementations22 Apr 2019 Chao Wu, Jun Xiao, Gang Huang, Fei Wu

Model training, as well as the communication, is achieved with blockchain and its smart contracts.

BIG-bench Machine Learning Position

Deep Sequence Learning with Auxiliary Information for Traffic Prediction

1 code implementation13 Jun 2018 Binbing Liao, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, Fei Wu

Predicting traffic conditions from online route queries is a challenging task as there are many complicated interactions over the roads and crowds involved.

Traffic Prediction

Generative Creativity: Adversarial Learning for Bionic Design

no code implementations19 May 2018 Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo

Bionic design refers to an approach of generative creativity in which a target object (e. g. a floor lamp) is designed to contain features of biological source objects (e. g. flowers), resulting in creative biologically-inspired design.

Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security

no code implementations20 Nov 2017 Hao Dong, Chao Wu, Zhen Wei, Yike Guo

However, current architecture of deep networks suffers the privacy issue that users need to give out their data to the model (typically hosted in a server or a cluster on Cloud) for training or prediction.

Anomaly Detection Decision Making +1

Semantic Image Synthesis via Adversarial Learning

2 code implementations ICCV 2017 Hao Dong, Simiao Yu, Chao Wu, Yike Guo

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e. g. intelligent image manipulation.

Image Generation Image Manipulation

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

8 code implementations12 Mar 2017 Akara Supratak, Hao Dong, Chao Wu, Yike Guo

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

EEG Sleep Stage Detection

Unsupervised Image-to-Image Translation with Generative Adversarial Networks

no code implementations10 Jan 2017 Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo

It's useful to automatically transform an image from its original form to some synthetic form (style, partial contents, etc.

Translation Unsupervised Image-To-Image Translation

Mixed Neural Network Approach for Temporal Sleep Stage Classification

no code implementations15 Oct 2016 Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M. Matthews, Yike Guo

Use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.

Classification EEG +1

Noise Robust IOA/CAS Speech Separation and Recognition System For The Third 'CHIME' Challenge

no code implementations21 Sep 2015 Xiaofei Wang, Chao Wu, Pengyuan Zhang, Ziteng Wang, Yong liu, Xu Li, Qiang Fu, Yonghong Yan

This paper presents the contribution to the third 'CHiME' speech separation and recognition challenge including both front-end signal processing and back-end speech recognition.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

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