Neural networks have achieved remarkable performance across various problem domains,
but their widespread applicability is hindered by inherent limitations such as overconfidence …

Sharpness-aware minimization (SAM) was proposed to reduce sharpness of minima and
has been shown to enhance generalization performance in various settings. In this work we …

Decentralized stochastic gradient descent (D-SGD) allows collaborative learning on
massive devices simultaneously without the control of a central server. However, existing …

Abstract Sharpness-Aware Minimization (SAM) is an optimizer that takes a descent step
based on the gradient at a perturbation $ y_t= x_t+\rho\frac {\nabla f (x_t)}{\lVert\nabla f …

Adversarial examples for deep neural networks (DNNs) are transferable: examples that
successfully fool one white-box surrogate model can also deceive other black-box models …

The problem of model selection is considered for the setting of interpolating estimators,
where the number of model parameters exceeds the size of the dataset. Classical …

We give extensive empirical evidence against the common belief that variational learning is
ineffective for large neural networks. We show that an optimizer called Improved Variational …

Transformer and its variants are a powerful class of architectures for sequential
recommendation, owing to their ability of capturing a user's dynamic interests from their past …

Abstract Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep
learning models by imposing a prior distribution over model parameters and inferring a …

Distributional robustness is a promising framework for training deep learning models that
are less vulnerable to adversarial examples and data distribution shifts. Previous works …