Data-driven AI & Machine Learning – Data Science and Analytics Thrust

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A Co-training Approach for Noisy Time Series Learning

Jia LI, Fugee TSUNG

Co-training, Contrastive Learning, Noisy Data, Time Series

A Fused Gromov-Wasserstein Framework for Unsupervised Knowledge Graph Entity Alignment

Fused Gromov-Wasserstein distance, knowledge graphs

A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots

Hyperparameter Stealing, t-SNE

All in One: Multi-Task Prompting for Graph Neural Networks

graph neural networks, Pre-training, Prompt Tuning

Alleviating Over-smoothing for Unsupervised Sentence Representation

Pre-trained Language Models, Self-Contrastive Learning

Auditing Membership Leakages of Multi-Exit Networks

Membership Inference Attack, Multi-Exit Networks

Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders

Contrastive Learning, Image Encoder, Model Stealing

Data Acquisition

Nan TANG, Yuyu LUO

acquisition, ML, training data

Data Matching

AI, matching

Data Poisoning Attacks Against Multimodal Encoders

Multimodal, Poisoning