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06/25/2023
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto metho...
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05/30/2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sparse training has received an upsurging interest in machine learning d...
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05/30/2023
Are Large Kernels Better Teachers than Transformers for ConvNets?
This paper reveals a new appeal of the recently emerged large-kernel Con...
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03/03/2023
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Sparse Neural Networks (SNNs) have received voluminous attention predomi...
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11/28/2022
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Recent works have impressively demonstrated that there exists a subnetwo...
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08/23/2022
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lottery tickets (LTs) is able to discover accurate and sparse subnetwork...
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05/30/2022
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Recent works on sparse neural network training (sparse training) have sh...
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10/01/2021
Calibrated Adversarial Training
Adversarial training is an approach of increasing the robustness of mode...
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07/06/2021
On Generalization of Graph Autoencoders with Adversarial Training
Adversarial training is an approach for increasing model's resilience ag...
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04/19/2021
Direction-Aggregated Attack for Transferable Adversarial Examples
Deep neural networks are vulnerable to adversarial examples that are cra...
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04/16/2021
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Recent years have witnessed an upsurge of interest in the problem of ano...
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11/07/2020
Bridging the Performance Gap between FGSM and PGD Adversarial Training
Deep learning achieves state-of-the-art performance in many tasks but ex...
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09/30/2020
