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09/14/2023
Gradient constrained sharpness-aware prompt learning for vision-language models
This paper targets a novel trade-off problem in generalizable prompt lea...
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07/19/2023
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Rare categories abound in a number of real-world networks and play a piv...
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07/17/2023
HeroLT: Benchmarking Heterogeneous Long-Tailed Learning
Long-tailed data distributions are prevalent in a variety of domains, in...
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06/25/2023
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
While graph heterophily has been extensively studied in recent years, a ...
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05/17/2023
Characterizing Long-Tail Categories on Graphs
Long-tail data distributions are prevalent in many real-world networks, ...
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05/10/2023
Quantization Aware Attack: Enhancing the Transferability of Adversarial Attacks across Target Models with Different Quantization Bitwidths
Quantized Neural Networks (QNNs) receive increasing attention in resourc...
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05/01/2023
Personalized Federated Learning under Mixture of Distributions
The recent trend towards Personalized Federated Learning (PFL) has garne...
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05/01/2023
Dynamic Transfer Learning across Graphs
Transferring knowledge across graphs plays a pivotal role in many high-s...
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12/09/2022
Towards High-Order Complementary Recommendation via Logical Reasoning Network
Complementary recommendation gains increasing attention in e-commerce si...
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12/09/2022
Augmenting Knowledge Transfer across Graphs
Given a resource-rich source graph and a resource-scarce target graph, h...
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10/19/2022
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning
Subgraph similarity search, one of the core problems in graph search, co...
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10/04/2022
Strength-Adaptive Adversarial Training
Adversarial training (AT) is proved to reliably improve network's robust...
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09/30/2022
Visual Privacy Protection Based on Type-I Adversarial Attack
With the development of online artificial intelligence systems, many dee...
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08/21/2022
MentorGNN: Deriving Curriculum for Pre-Training GNNs
Graph pre-training strategies have been attracting a surge of attention ...
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07/25/2022
Improving Adversarial Robustness via Mutual Information Estimation
Deep neural networks (DNNs) are found to be vulnerable to adversarial no...
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09/21/2021
Modelling Adversarial Noise for Adversarial Defense
Deep neural networks have been demonstrated to be vulnerable to adversar...
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06/10/2021
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Deep neural networks (DNNs) are vulnerable to adversarial noise. A range...
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06/09/2021
Towards Defending against Adversarial Examples via Attack-Invariant Features
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their a...
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04/19/2021
