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06/09/2023
One-Shot Machine Unlearning with Mnemonic Code
Deep learning has achieved significant improvements in accuracy and has ...
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06/09/2023
Revisiting Permutation Symmetry for Merging Models between Different Datasets
Model merging is a new approach to creating a new model by combining the...
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11/01/2022
ARDIR: Improving Robustness using Knowledge Distillation of Internal Representation
Adversarial training is the most promising method for learning robust mo...
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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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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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02/05/2021
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
Adversarial training is actively studied for learning robust models agai...
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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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03/26/2019
Autoencoding Binary Classifiers for Supervised Anomaly Detection
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised...
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03/22/2019
Macro Action Reinforcement Learning with Sequence Disentanglement using Variational Autoencoder
One problem in the application of reinforcement learning to real-world p...
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02/22/2019
FAVAE: Sequence Disentanglement using Information Bottleneck Principle
We propose the factorized action variational autoencoder (FAVAE), a stat...
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09/14/2018
