Search Results for author: Min Wu

Found 116 papers, 40 papers with code

LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices

no code implementations13 Jun 2024 Ruibing Jin, Qing Xu, Min Wu, Yuecong Xu, Dan Li, XiaoLi Li, Zhenghua Chen

To address this issue, we propose Knowledge Pruning (KP), a novel paradigm for time series learning in this paper.

Enhancing Tabular Data Optimization with a Flexible Graph-based Reinforced Exploration Strategy

no code implementations11 Jun 2024 Xiaohan Huang, Dongjie Wang, Zhiyuan Ning, Ziyue Qiao, Qingqing Long, Haowei Zhu, Min Wu, Yuanchun Zhou, Meng Xiao

Tabular data optimization methods aim to automatically find an optimal feature transformation process that generates high-value features and improves the performance of downstream machine learning tasks.

Decision Making Feature Engineering

Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization

no code implementations11 Jun 2024 Weiliang Zhang, Zhen Meng, Dongjie Wang, Min Wu, Kunpeng Liu, Yuanchun Zhou, Meng Xiao

In this study, we introduce an iterative gene panel selection strategy that is applicable to clustering tasks in single-cell genomics.

Reinforcement Learning (RL)

MLLM-SR: Conversational Symbolic Regression base Multi-Modal Large Language Models

no code implementations8 Jun 2024 YanJie Li, Weijun Li, Lina Yu, Min Wu, Jingyi Liu, Wenqiang Li, Shu Wei, Yusong Deng

The existing symbolic regression methods directly generate expressions according to the given observation data, and we cannot require the algorithm to generate expressions that meet specific requirements according to the known prior knowledge.

regression Symbolic Regression

Closed-form Symbolic Solutions: A New Perspective on Solving Partial Differential Equations

no code implementations23 May 2024 Shu Wei, YanJie Li, Lina Yu, Min Wu, Weijun Li, Meilan Hao, Wenqiang Li, Jingyi Liu, Yusong Deng

Solving partial differential equations (PDEs) in Euclidean space with closed-form symbolic solutions has long been a dream for mathematicians.

Temporal and Heterogeneous Graph Neural Network for Remaining Useful Life Prediction

no code implementations7 May 2024 Zhihao Wen, Yuan Fang, Pengcheng Wei, Fayao Liu, Zhenghua Chen, Min Wu

To capture the nuances of the temporal and spatial relationships and heterogeneous characteristics in an interconnected graph of sensors, we introduce a novel model named Temporal and Heterogeneous Graph Neural Networks (THGNN).

Graph Neural Network

TSLANet: Rethinking Transformers for Time Series Representation Learning

1 code implementation12 Apr 2024 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, XiaoLi Li

Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications.

Anomaly Detection Computational Efficiency +4

Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning

no code implementations9 Apr 2024 YanJie Li, Weijun Li, Lina Yu, Min Wu, Jingyi Liu, Wenqiang Li, Meilan Hao, Shu Wei, Yusong Deng

However, its performance is very dependent on the training data and performs poorly on data outside the training set, which leads to poor noise robustness and Versatility of such methods.

Combinatorial Optimization regression +2

Balancing Fairness and Accuracy in Data-Restricted Binary Classification

no code implementations12 Mar 2024 Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou, Ivan Brugere, Dana Dachman-Soled, Min Wu

Applications that deal with sensitive information may have restrictions placed on the data available to a machine learning (ML) classifier.

Attribute Binary Classification +1

K-Link: Knowledge-Link Graph from LLMs for Enhanced Representation Learning in Multivariate Time-Series Data

no code implementations6 Mar 2024 Yucheng Wang, Ruibing Jin, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen

To capture these dependencies, Graph Neural Networks (GNNs) have emerged as powerful tools, yet their effectiveness is restricted by the quality of graph construction from MTS data.

General Knowledge graph construction +2

PowerSkel: A Device-Free Framework Using CSI Signal for Human Skeleton Estimation in Power Station

1 code implementation4 Mar 2024 Cunyi Yin, Xiren Miao, Jing Chen, Hao Jiang, Jianfei Yang, Yunjiao Zhou, Min Wu, Zhenghua Chen

WiFi-based human pose estimation is a suitable method for monitoring power operations due to its low cost, device-free, and robustness to various illumination conditions. In this paper, a novel Channel State Information (CSI)-based pose estimation framework, namely PowerSkel, is developed to address these challenges.

Knowledge Distillation Pose Estimation

MMSR: Symbolic Regression is a Multimodal Task

no code implementations28 Feb 2024 YanJie Li, Jingyi Liu, Weijun Li, Lina Yu, Min Wu, Wenqiang Li, Meilan Hao, Su Wei, Yusong Deng

The SR problem is solved as a pure multimodal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion.

Combinatorial Optimization Contrastive Learning +2

Balancing Spectral, Temporal and Spatial Information for EEG-based Alzheimer's Disease Classification

no code implementations21 Feb 2024 Stephan Goerttler, Fei He, Min Wu

Here, we investigate the importance of spatial information relative to spectral or temporal information by varying the proportion of each dimension for AD classification.

Classification EEG

Stochastic Graph Heat Modelling for Diffusion-based Connectivity Retrieval

no code implementations20 Feb 2024 Stephan Goerttler, Fei He, Min Wu

Here, we combine the graph heat equation with the stochastic heat equation, which ultimately yields a model for multivariate time signals on a graph.


Self-evolving Autoencoder Embedded Q-Network

no code implementations18 Feb 2024 J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal, Rajdeep Dutta, Ji Wei Yoon, Aye Phyu Phyu Aung, Keyu Wu, Min Wu, XiaoLi Li

During the evolution of the autoencoder architecture, a bias-variance regulatory strategy is employed to elicit the optimal response from the RL agent.

Decision Making Reinforcement Learning (RL)

Evolving Restricted Boltzmann Machine-Kohonen Network for Online Clustering

no code implementations14 Feb 2024 J. Senthilnath, Adithya Bhattiprolu, Ankur Singh, Bangjian Zhou, Min Wu, Jón Atli Benediktsson, XiaoLi Li

A novel online clustering algorithm is presented where an Evolving Restricted Boltzmann Machine (ERBM) is embedded with a Kohonen Network called ERBM-KNet.

Clustering Online Clustering

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data

no code implementations6 Feb 2024 Yvonne Zhou, Mingyu Liang, Ivan Brugere, Dana Dachman-Soled, Danial Dervovic, Antigoni Polychroniadou, Min Wu

The growing use of machine learning (ML) has raised concerns that an ML model may reveal private information about an individual who has contributed to the training dataset.

PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression

1 code implementation25 Jan 2024 Min Wu, Weijun Li, Lina Yu, Wenqiang Li, Jingyi Liu, YanJie Li, Meilan Hao

Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting.

Interpretable Machine Learning regression +1

Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search

no code implementations24 Jan 2024 YanJie Li, Weijun Li, Lina Yu, Min Wu, Jingyi Liu, Wenqiang Li, Meilan Hao, Shu Wei, Yusong Deng

To optimize the trade-off between efficiency and versatility, we introduce SR-GPT, a novel algorithm for symbolic regression that integrates Monte Carlo Tree Search (MCTS) with a Generative Pre-Trained Transformer (GPT).

regression Symbolic Regression

Explaining Time Series via Contrastive and Locally Sparse Perturbations

1 code implementation16 Jan 2024 Zichuan Liu, Yingying Zhang, Tianchun Wang, Zefan Wang, Dongsheng Luo, Mengnan Du, Min Wu, Yi Wang, Chunlin Chen, Lunting Fan, Qingsong Wen

Explaining multivariate time series is a compound challenge, as it requires identifying important locations in the time series and matching complex temporal patterns.

Contrastive Learning counterfactual +1

A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure

no code implementations3 Jan 2024 YanJie Li, Weijun Li, Lina Yu, Min Wu, Jinyi Liu, Wenqiang Li, Meilan Hao

1, The type of activation function is single and relatively fixed, which leads to poor "unit representation ability" of the network, and it is often used to solve simple problems with very complex networks; 2, the network structure is not adaptive, it is easy to cause network structure redundant or insufficient.

Towards Efficient Verification of Quantized Neural Networks

1 code implementation20 Dec 2023 Pei Huang, Haoze Wu, Yuting Yang, Ieva Daukantas, Min Wu, Yedi Zhang, Clark Barrett

Quantization replaces floating point arithmetic with integer arithmetic in deep neural network models, providing more efficient on-device inference with less power and memory.


Understanding Concepts in Graph Signal Processing for Neurophysiological Signal Analysis

no code implementations6 Dec 2023 Stephan Goerttler, Fei He, Min Wu

The experimental section focuses on the role of graph frequency in data classification, with applications to neuroimaging.

MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations

no code implementations13 Nov 2023 YanJie Li, Weijun Li, Lina Yu, Min Wu, Jinyi Liu, Wenqiang Li, Meilan Hao, Shu Wei, Yusong Deng

To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction.

regression Symbolic Regression

A Canonical Data Transformation for Achieving Inter- and Within-group Fairness

no code implementations23 Oct 2023 Zachary McBride Lazri, Ivan Brugere, Xin Tian, Dana Dachman-Soled, Antigoni Polychroniadou, Danial Dervovic, Min Wu

The mapping is constructed to preserve the relative relationship between the scores obtained from the unprocessed feature vectors of individuals from the same demographic group, guaranteeing within-group fairness.


Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition

no code implementations22 Oct 2023 Hongxiang Gao, Xiangyao Wang, Zhenghua Chen, Min Wu, Zhipeng Cai, Lulu Zhao, Jianqing Li, Chengyu Liu

To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena.

EEG Emotion Recognition

Graph Neural Network-based EEG Classification: A Survey

no code implementations3 Oct 2023 Dominik Klepl, Min Wu, Fei He

Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders.

Classification EEG +4

A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data

1 code implementation24 Sep 2023 Wenqiang Li, Weijun Li, Lina Yu, Min Wu, Linjun Sun, Jingyi Liu, YanJie Li, Shu Wei, Yusong Deng, Meilan Hao

Instead of searching for expressions within a large search space, we explore symbolic networks with various structures, guided by reinforcement learning, and optimize them to identify expressions that better-fitting the data.

Symbolic Regression

Information Forensics and Security: A quarter-century-long journey

no code implementations21 Sep 2023 Mauro Barni, Patrizio Campisi, Edward J. Delp, Gwenael Doërr, Jessica Fridrich, Nasir Memon, Fernando Pérez-González, Anderson Rocha, Luisa Verdoliva, Min Wu

Information Forensics and Security (IFS) is an active R&D area whose goal is to ensure that people use devices, data, and intellectual properties for authorized purposes and to facilitate the gathering of solid evidence to hold perpetrators accountable.

Graph-Aware Contrasting for Multivariate Time-Series Classification

1 code implementation11 Sep 2023 Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen

As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data.

Classification Contrastive Learning +3

Fully-Connected Spatial-Temporal Graph for Multivariate Time-Series Data

1 code implementation11 Sep 2023 Yucheng Wang, Yuecong Xu, Jianfei Yang, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen

For graph construction, we design a decay graph to connect sensors across all timestamps based on their temporal distances, enabling us to fully model the ST dependencies by considering the correlations between DEDT.

graph construction Graph Neural Network +1

Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks

2 code implementations ICCV 2023 Kaixin Xu, Zhe Wang, Xue Geng, Jie Lin, Min Wu, XiaoLi Li, Weisi Lin

On ImageNet, we achieve up to 4. 7% and 4. 6% higher top-1 accuracy compared to other methods for VGG-16 and ResNet-50, respectively.

Combinatorial Optimization

Source-Free Domain Adaptation with Temporal Imputation for Time Series Data

1 code implementation14 Jul 2023 Mohamed Ragab, Emadeldeen Eldele, Min Wu, Chuan-Sheng Foo, XiaoLi Li, Zhenghua Chen

The existing SFDA methods that are mainly designed for visual applications may fail to handle the temporal dynamics in time series, leading to impaired adaptation performance.

Imputation Source-Free Domain Adaptation +1

AutoHint: Automatic Prompt Optimization with Hint Generation

1 code implementation13 Jul 2023 Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, Denis Charles

This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM).

Hint Generation In-Context Learning +2

Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data

1 code implementation7 Jul 2023 Qing Xu, Min Wu, XiaoLi Li, Kezhi Mao, Zhenghua Chen

More specifically, a feature-domain discriminator is employed to align teacher's and student's representations for universal knowledge transfer.

Knowledge Distillation Model Compression +2

Revisiting Computer-Aided Tuberculosis Diagnosis

1 code implementation6 Jul 2023 Yun Liu, Yu-Huan Wu, Shi-Chen Zhang, Li Liu, Min Wu, Ming-Ming Cheng

This dataset enables the training of sophisticated detectors for high-quality CTD.

Image Classification

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

1 code implementation29 Jun 2023 Meng Xiao, Dongjie Wang, Min Wu, Kunpeng Liu, Hui Xiong, Yuanchun Zhou, Yanjie Fu

Feature transformation aims to reconstruct an effective representation space by mathematically refining the existing features.

Feature Engineering Q-Learning

A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

no code implementations28 Jun 2023 Fakai Wang, Chi-Tung Cheng, Chien-Wei Peng, Ke Yan, Min Wu, Le Lu, Chien-Hung Liao, Ling Zhang

In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1, 631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks.

Lesion Detection Specificity

Incorporating Prior Knowledge in Deep Learning Models via Pathway Activity Autoencoders

1 code implementation9 Jun 2023 Pedro Henrique da Costa Avelar, Min Wu, Sophia Tsoka

Through comprehensive comparisons among various learning models, we show that, despite having access to a smaller set of features, our PAAE and PAVAE models achieve better out-of-set reconstruction results compared to common methodologies.

ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning

no code implementations10 Apr 2023 Hongxiang Gao, Xingyao Wang, Zhenghua Chen, Min Wu, Jianqing Li, Chengyu Liu

From the perspective of intelligent wearable applications, the possibility of a comprehensive ECG interpretation algorithm based on single-lead ECGs is also confirmed.

Continual Learning Incremental Learning +1

Augmenting and Aligning Snippets for Few-Shot Video Domain Adaptation

no code implementations ICCV 2023 Yuecong Xu, Jianfei Yang, Yunjiao Zhou, Zhenghua Chen, Min Wu, XiaoLi Li

We thus consider a more realistic \textit{Few-Shot Video-based Domain Adaptation} (FSVDA) scenario where we adapt video models with only a few target video samples.

Action Recognition Unsupervised Domain Adaptation

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting

1 code implementation11 Mar 2023 Simon Graham, Quoc Dang Vu, Mostafa Jahanifar, Martin Weigert, Uwe Schmidt, Wenhua Zhang, Jun Zhang, Sen yang, Jinxi Xiang, Xiyue Wang, Josef Lorenz Rumberger, Elias Baumann, Peter Hirsch, Lihao Liu, Chenyang Hong, Angelica I. Aviles-Rivero, Ayushi Jain, Heeyoung Ahn, Yiyu Hong, Hussam Azzuni, Min Xu, Mohammad Yaqub, Marie-Claire Blache, Benoît Piégu, Bertrand Vernay, Tim Scherr, Moritz Böhland, Katharina Löffler, Jiachen Li, Weiqin Ying, Chixin Wang, Dagmar Kainmueller, Carola-Bibiane Schönlieb, Shuolin Liu, Dhairya Talsania, Yughender Meda, Prakash Mishra, Muhammad Ridzuan, Oliver Neumann, Marcel P. Schilling, Markus Reischl, Ralf Mikut, Banban Huang, Hsiang-Chin Chien, Ching-Ping Wang, Chia-Yen Lee, Hong-Kun Lin, Zaiyi Liu, Xipeng Pan, Chu Han, Jijun Cheng, Muhammad Dawood, Srijay Deshpande, Raja Muhammad Saad Bashir, Adam Shephard, Pedro Costa, João D. Nunes, Aurélio Campilho, Jaime S. Cardoso, Hrishikesh P S, Densen Puthussery, Devika R G, Jiji C V, Ye Zhang, Zijie Fang, Zhifan Lin, Yongbing Zhang, Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Vi Thi-Tuong Vo, Soo-Hyung Kim, Taebum Lee, Satoshi Kondo, Satoshi Kasai, Pranay Dumbhare, Vedant Phuse, Yash Dubey, Ankush Jamthikar, Trinh Thi Le Vuong, Jin Tae Kwak, Dorsa Ziaei, Hyun Jung, Tianyi Miao, David Snead, Shan E Ahmed Raza, Fayyaz Minhas, Nasir M. Rajpoot

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.

Nuclear Segmentation Segmentation +2

MetaGrad: Adaptive Gradient Quantization with Hypernetworks

no code implementations4 Mar 2023 Kaixin Xu, Alina Hui Xiu Lee, Ziyuan Zhao, Zhe Wang, Min Wu, Weisi Lin

A popular track of network compression approach is Quantization aware Training (QAT), which accelerates the forward pass during the neural network training and inference.


Convex Bounds on the Softmax Function with Applications to Robustness Verification

1 code implementation3 Mar 2023 Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, Eitan Farchi

The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well.

Beyond Discrete Selection: Continuous Embedding Space Optimization for Generative Feature Selection

no code implementations26 Feb 2023 Meng Xiao, Dongjie Wang, Min Wu, Pengfei Wang, Yuanchun Zhou, Yanjie Fu

Furthermore, we reconstruct feature selection solutions using these embeddings and select the feature subset with the highest performance for downstream tasks as the optimal subset.

Decoder feature selection

Label-efficient Time Series Representation Learning: A Review

no code implementations13 Feb 2023 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li

The scarcity of labeled data is one of the main challenges of applying deep learning models on time series data in the real world.

Representation Learning Self-Supervised Learning +3

Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

no code implementations bioRxiv 2023 Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Zeng Li, Huazhu Fu, Min Wu, Hsiu Kim Lina Lim, Longqi Liu, Jinmiao Chen

Lastly, compared to other methods, GraphST’s cell type deconvolution achieved higher accuracy on simulated data and better captured spatial niches such as the germinal centers of the lymph node in experimentally acquired data.

Clustering Contrastive Learning

RWSC-Fusion: Region-Wise Style-Controlled Fusion Network for the Prohibited X-Ray Security Image Synthesis

no code implementations CVPR 2023 Luwen Duan, Min Wu, Lijian Mao, Jun Yin, Jianping Xiong, Xi Li

Automatic prohibited item detection in security inspection X-ray images is necessary for transportation. The abundance and diversity of the X-ray security images with prohibited item, termed as prohibited X-ray security images, are essential for training the detection model.

Image Generation

Traceable Automatic Feature Transformation via Cascading Actor-Critic Agents

1 code implementation27 Dec 2022 Meng Xiao, Dongjie Wang, Min Wu, Ziyue Qiao, Pengfei Wang, Kunpeng Liu, Yuanchun Zhou, Yanjie Fu

Feature transformation for AI is an essential task to boost the effectiveness and interpretability of machine learning (ML).

feature selection

On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

no code implementations14 Dec 2022 Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao

To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.


Contrastive Domain Adaptation for Time-Series via Temporal Mixup

1 code implementation3 Dec 2022 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li

Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains.

Contrastive Learning Time Series +2

Self-Optimizing Feature Transformation

no code implementations16 Sep 2022 Meng Xiao, Dongjie Wang, Min Wu, Kunpeng Liu, Hui Xiong, Yuanchun Zhou, Yanjie Fu

Feature transformation aims to extract a good representation (feature) space by mathematically transforming existing features.

Feature Engineering Outlier Detection

SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance

no code implementations30 Aug 2022 Li Lyna Zhang, Youkow Homma, Yujing Wang, Min Wu, Mao Yang, Ruofei Zhang, Ting Cao, Wei Shen

Remarkably, under our latency requirement of 1900us on CPU, SwiftPruner achieves a 0. 86% higher AUC than the state-of-the-art uniform sparse baseline for BERT-Mini on a large scale real-world dataset.

Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

2 code implementations13 Aug 2022 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module.

Contrastive Learning Data Augmentation +6

Leveraging Endo- and Exo-Temporal Regularization for Black-box Video Domain Adaptation

no code implementations10 Aug 2022 Yuecong Xu, Jianfei Yang, Haozhi Cao, Min Wu, XiaoLi Li, Lihua Xie, Zhenghua Chen

To enable video models to be applied seamlessly across video tasks in different environments, various Video Unsupervised Domain Adaptation (VUDA) methods have been proposed to improve the robustness and transferability of video models.

Action Recognition Unsupervised Domain Adaptation

A Generalized Probabilistic Monitoring Model with Both Random and Sequential Data

no code implementations27 Jun 2022 Wanke Yu, Min Wu, Biao Huang, Chengda Lu

Many multivariate statistical analysis methods and their corresponding probabilistic counterparts have been adopted to develop process monitoring models in recent decades.

Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders

1 code implementation21 Jun 2022 Pedro Henrique da Costa Avelar, Roman Laddach, Sophia Karagiannis, Min Wu, Sophia Tsoka

We also perform a feature selection stability analysis on our models and notice that there is a power-law relationship with features which are commonly associated with survival.

Data Integration feature selection

Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease

no code implementations10 Jun 2022 Dominik Klepl, Fei He, Min Wu, Daniel J. Blackburn, Ptolemaios G. Sarrigiannis

Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands.

Classification EEG

A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges

no code implementations8 May 2022 Zhenghua Chen, Min Wu, Alvin Chan, XiaoLi Li, Yew-Soon Ong

We believe that this technical review can help to promote a sustainable development of AI R&D activities for the research community.


Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction

no code implementations25 Apr 2022 Abdul Rehman, Zhen-Tao Liu, Min Wu, Wei-Hua Cao, Cheng-Shan Jiang

A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide predictions.

Cross-corpus Sentence +1

RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System

no code implementations14 Apr 2022 Muhammed Zahid Ozturk, Chenshu Wu, Beibei Wang, Min Wu, K. J. Ray Liu

Speech enhancement and separation have been a long-standing problem, especially with the recent advances using a single microphone.

Speech Enhancement Speech Separation

Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction

1 code implementation22 Mar 2022 Zipeng Zhong, Jie Song, Zunlei Feng, Tiantao Liu, Lingxiang Jia, Shaolun Yao, Min Wu, Tingjun Hou, Mingli Song

Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.

Chemical Reaction Prediction Retrosynthesis +1

ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data

1 code implementation15 Mar 2022 Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li

Our evaluation includes adapting state-of-the-art visual domain adaptation methods to time series data as well as the recent methods specifically developed for time series data.

Benchmarking Time Series +2

Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting

no code implementations1 Mar 2022 Chunhui Lin, Liukun Zhang, Lijian Mao, Min Wu, Dong Hu

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath).

Classification Explainable Models +2

Lumbar Bone Mineral Density Estimation from Chest X-ray Images: Anatomy-aware Attentive Multi-ROI Modeling

no code implementations5 Jan 2022 Fakai Wang, Kang Zheng, Le Lu, Jing Xiao, Min Wu, Chang-Fu Kuo, Shun Miao

Osteoporosis is a common chronic metabolic bone disease often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, e. g. via Dual-energy X-ray Absorptiometry (DXA).

Anatomy Density Estimation

Motif Graph Neural Network

1 code implementation30 Dec 2021 Xuexin Chen, Ruichu Cai, Yuan Fang, Min Wu, Zijian Li, Zhifeng Hao

However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing \emph{high-order} graph structures as opposed to \emph{low-order} structures.

Graph Classification Graph Embedding +3

Self-supervised Autoregressive Domain Adaptation for Time Series Data

1 code implementation29 Nov 2021 Mohamed Ragab, Emadeldeen Eldele, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li

Second, we propose a novel autoregressive domain adaptation technique that incorporates temporal dependency of both source and target features during domain alignment.

Self-Supervised Learning Time Series +2

A Systematic Evaluation of Domain Adaptation Algorithms On Time Series Data

no code implementations29 Sep 2021 Mohamed Ragab, Emadeldeen Eldele, Wee Ling Tan, Chuan-Sheng Foo, Zhenghua Chen, Min Wu, Chee Kwoh, XiaoLi Li

Our evaluation includes adaptations of state-of-the-art visual domain adaptation methods to time series data in addition to recent methods specifically developed for time series data.

Benchmarking Model Selection +3

Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment

no code implementations21 Sep 2021 Yuecong Xu, Jianfei Yang, Haozhi Cao, Keyu Wu, Min Wu, Rui Zhao, Zhenghua Chen

Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios.

Unsupervised Domain Adaptation

A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras

no code implementations18 Jul 2021 Xin Tian, Chau-Wai Wong, Sushant M. Ranadive, Min Wu

Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1. 26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%.

Remote Blood Oxygen Estimation From Videos Using Neural Networks

no code implementations11 Jul 2021 Joshua Mathew, Xin Tian, Min Wu, Chau-Wai Wong

Blood oxygen saturation (SpO$_2$) is an essential indicator of respiratory functionality and is receiving increasing attention during the COVID-19 pandemic.

ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

1 code implementation9 Jul 2021 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.

Automatic Sleep Stage Classification Domain Adaptation +2

Time-Series Representation Learning via Temporal and Contextual Contrasting

1 code implementation26 Jun 2021 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, XiaoLi Li, Cuntai Guan

In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.

Automatic Sleep Stage Classification Contrastive Learning +9

Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals

no code implementations3 May 2021 Ravi Garg, Adi Hajj-Ahmad, Min Wu

In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces.

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

1 code implementation28 Apr 2021 Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.

Automatic Sleep Stage Classification EEG +1

Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray

no code implementations5 Apr 2021 Fakai Wang, Kang Zheng, Yirui Wang, XiaoYun Zhou, Le Lu, Jing Xiao, Min Wu, Chang-Fu Kuo, Shun Miao

In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations.

FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data

no code implementations6 Feb 2021 Yuxiao Lu, Jie Lin, Chao Jin, Zhe Wang, Min Wu, Khin Mi Mi Aung, XiaoLi Li

Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures.

Privacy Preserving

Cross-domain Joint Dictionary Learning for ECG Inference from PPG

no code implementations7 Jan 2021 Xin Tian, Qiang Zhu, Yuenan Li, Min Wu

The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring.

Clinical Knowledge Dictionary Learning

Automatic Vertebra Localization and Identification in CT by Spine Rectification and Anatomically-constrained Optimization

no code implementations CVPR 2021 Fakai Wang, Kang Zheng, Le Lu, Jing Xiao, Min Wu, Shun Miao

This paper proposes a robust and accurate method that effectively exploits the anatomical knowledge of the spine to facilitate vertebra localization and identification.

Inferring ECG from PPG for Continuous Cardiac Monitoring Using Lightweight Neural Network

no code implementations9 Dec 2020 Yuenan Li, Xin Tian, Qiang Zhu, Min Wu

This paper presents a computational solution that enables continuous cardiac monitoring through cross-modality inference of electrocardiogram (ECG).

Model Compression

Attention Sequence to Sequence Model for Machine Remaining Useful Life Prediction

no code implementations20 Jul 2020 Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, Ruqiang Yan, Xiao-Li Li

Accurate estimation of remaining useful life (RUL) of industrial equipment can enable advanced maintenance schedules, increase equipment availability and reduce operational costs.


Recent Advances in Network-based Methods for Disease Gene Prediction

1 code implementation19 Jul 2020 Sezin Kircali Ata, Min Wu, Yuan Fang, Le Ou-Yang, Chee Keong Kwoh, Xiao-Li Li

Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases.

Graph Representation Learning

Transparency Tools for Fairness in AI (Luskin)

no code implementations9 Jul 2020 Mingliang Chen, Aria Shahverdi, Sarah Anderson, Se Yong Park, Justin Zhang, Dana Dachman-Soled, Kristin Lauter, Min Wu

The three tools are: - A new definition of fairness called "controlled fairness" with respect to choices of protected features and filters.


Towards Threshold Invariant Fair Classification

no code implementations18 Jun 2020 Mingliang Chen, Min Wu

This paper introduces the notion of threshold invariant fairness, which enforces equitable performances across different groups independent of the decision threshold.

BIG-bench Machine Learning Classification +2

Multi-View Collaborative Network Embedding

3 code implementations17 May 2020 Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiao-Li Li

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.

Network Embedding

Adaptive Multi-Trace Carving for Robust Frequency Tracking in Forensic Applications

no code implementations14 May 2020 Qiang Zhu, Mingliang Chen, Chau-Wai Wong, Min Wu

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals.

Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

no code implementations24 Apr 2020 Susen Yang, Yong liu, Yonghui Xu, Chunyan Miao, Min Wu, Juyong Zhang

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation.

Graph Attention

Respiratory Rate Estimation from Face Videos

no code implementations8 Sep 2019 Mingliang Chen, Qiang Zhu, Harrison Zhang, Min Wu, Quanzeng Wang

Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos.

Heart Rate Variability Motion Compensation +1

A Deep Framework for Bone Age Assessment based on Finger Joint Localization

no code implementations7 May 2019 Xiaoman Zhang, Ziyuan Zhao, Cen Chen, Songyou Peng, Min Wu, Zhongyao Cheng, Singee Teo, Le Zhang, Zeng Zeng

In this study, we applied powerful deep neural network and explored a process in the forecast of skeletal bone age with the specifically combine joints images to increase the performance accuracy compared with the whole hand images.

MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation

no code implementations21 Apr 2019 Chaofan Tao, Fengmao Lv, Lixin Duan, Min Wu

Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples.

Domain Adaptation

SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

no code implementations20 Oct 2018 Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng

Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

1 code implementation10 Jul 2018 Min Wu, Matthew Wicker, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska

In this paper, we study two variants of pointwise robustness, the maximum safe radius problem, which for a given input sample computes the minimum distance to an adversarial example, and the feature robustness problem, which aims to quantify the robustness of individual features to adversarial perturbations.

Adversarial Attack Adversarial Defense +2

Concolic Testing for Deep Neural Networks

2 code implementations30 Apr 2018 Youcheng Sun, Min Wu, Wenjie Ruan, Xiaowei Huang, Marta Kwiatkowska, Daniel Kroening

Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program.

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm

2 code implementations16 Apr 2018 Wenjie Ruan, Min Wu, Youcheng Sun, Xiaowei Huang, Daniel Kroening, Marta Kwiatkowska

In this paper we focus on the $L_0$ norm and aim to compute, for a trained DNN and an input, the maximal radius of a safe norm ball around the input within which there are no adversarial examples.

Adaptive Cost-sensitive Online Classification

no code implementations6 Apr 2018 Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, Junzhou Huang

Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.

Anomaly Detection Classification +2

Safety Verification of Deep Neural Networks

2 code implementations21 Oct 2016 Xiaowei Huang, Marta Kwiatkowska, Sen Wang, Min Wu

Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations.

Adversarial Attack Adversarial Defense +3

Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution

no code implementations20 Oct 2016 Abbas Kazemipour, Ji Liu, Patrick Kanold, Min Wu, Behtash Babadi

In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events.

Sampling Requirements for Stable Autoregressive Estimation

no code implementations4 May 2016 Abbas Kazemipour, Sina Miran, Piya Pal, Behtash Babadi, Min Wu

Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime.

Model Selection

Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling

1 code implementation14 Jul 2015 Abbas Kazemipour, Min Wu, Behtash Babadi

We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate.

Multi-label ensemble based on variable pairwise constraint projection

no code implementations8 Mar 2014 Ping Li, Hong Li, Min Wu

For the boosting-like strategy, we employ both the variable pairwise constraints and the bootstrap steps to diversify the base classifiers.

Classification General Classification +1

Sparse Norm Filtering

no code implementations17 May 2013 Chengxi Ye, DaCheng Tao, Mingli Song, David W. Jacobs, Min Wu

Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.

Colorization Deblurring +2

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