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03/14/2023
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Regularized discrete optimal transport (OT) is a powerful tool to measur...
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10/04/2022
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks
Gate functions in recurrent models, such as an LSTM and GRU, play a cent...
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07/21/2022
Switching One-Versus-the-Rest Loss to Increase the Margin of Logits for Adversarial Robustness
Defending deep neural networks against adversarial examples is a key cha...
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05/31/2022
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Few-shot learning for neural networks (NNs) is an important problem that...
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06/17/2021
Pruning Randomly Initialized Neural Networks with Iterative Randomization
Pruning the weights of randomly initialized neural networks plays an imp...
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03/02/2021
Smoothness Analysis of Loss Functions of Adversarial Training
Deep neural networks are vulnerable to adversarial attacks. Recent studi...
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10/06/2020
Constraining Logits by Bounded Function for Adversarial Robustness
We propose a method for improving adversarial robustness by addition of ...
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09/19/2019
Absum: Simple Regularization Method for Reducing Structural Sensitivity of Convolutional Neural Networks
We propose Absum, which is a regularization method for improving adversa...
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06/10/2019
Network Implosion: Effective Model Compression for ResNets via Static Layer Pruning and Retraining
Residual Networks with convolutional layers are widely used in the field...
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05/31/2016
