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Found 7 papers, 1 papers with code
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
This paper proposes a new loss function for adversarial training.
Smoothness Analysis of Adversarial Training
We reveal that the constraint of adversarial attacks is one cause of the non-smoothness and that the smoothness depends on the types of the constraints.
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression
We theoretically and experimentally confirm that the weight loss landscape becomes sharper as the magnitude of the noise of adversarial training increases in the linear logistic regression model.
Constraining Logits by Bounded Function for Adversarial Robustness
We theoretically and empirically reveal that small logits by addition of a common activation function, e. g., hyperbolic tangent, do not improve adversarial robustness since input vectors of the function (pre-logit vectors) can have large norms.
Interdependencies of female board member appointments
We investigate the networks of Japanese corporate boards and its influence on the appointments of female board members.
Autoencoding Binary Classifiers for Supervised Anomaly Detection
Since our approach becomes able to reconstruct the normal data points accurately and fails to reconstruct the known and unknown anomalies, it can accurately discriminate both known and unknown anomalies from normal data points.
Variational Autoencoder with Implicit Optimal Priors
However, KL divergence with the aggregated posterior cannot be calculated in a closed form, which prevents us from using this optimal prior.
