The first integrated taxonomy, empirical study of interplay and shallow dememorization, plus a theoretical guarantee on dememorization depth for certified unlearning.
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PACO provides a hierarchical online decision system with proxy-simulated initial thresholds and adaptive updates from mature prototypes to enable consistent category discovery in streaming sequences.
IRNet uses per-layer residual shortcuts in fully connected networks to achieve better prediction accuracy and training convergence than prior ML methods on OQMD and Materials Project datasets for material properties.
Introduces an eight-class taxonomy for semantic image-text relations based on three metrics and a multimodal embedding model for predicting the classes from collected data.
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
A new deep hierarchical knowledge loss (DHK) with tree and triplet components improves fault intensity diagnosis by modeling class hierarchies on industrial datasets.
citing papers explorer
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SoK: Unlearnability and Unlearning for Model Dememorization
The first integrated taxonomy, empirical study of interplay and shallow dememorization, plus a theoretical guarantee on dememorization depth for certified unlearning.
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PACO: Proxy-Task Alignment and Online Calibration for On-the-Fly Category Discovery
PACO provides a hierarchical online decision system with proxy-simulated initial thresholds and adaptive updates from mature prototypes to enable consistent category discovery in streaming sequences.
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IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery
IRNet uses per-layer residual shortcuts in fully connected networks to achieve better prediction accuracy and training convergence than prior ML methods on OQMD and Materials Project datasets for material properties.
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Understanding, Categorizing and Predicting Semantic Image-Text Relations
Introduces an eight-class taxonomy for semantic image-text relations based on three metrics and a multimodal embedding model for predicting the classes from collected data.
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Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget
Checkerboard derives a closed-form checkerboard trigger for clean-label backdoor attacks that achieves over 94% ASR with poisoning rates as low as 0.46% on ImageNet-100 and 99.99% ASR with 20 samples on CIFAR-10.
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Deep Hierarchical Knowledge Loss for Fault Intensity Diagnosis
A new deep hierarchical knowledge loss (DHK) with tree and triplet components improves fault intensity diagnosis by modeling class hierarchies on industrial datasets.