Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

This paper is an attempt to bridge the conceptual gaps between researchers working on the
two widely used approaches based on positive definite kernels: Bayesian learning or …

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data.
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …

A concise and self-contained introduction to causal inference, increasingly important in data
science and machine learning. The mathematization of causality is a relatively recent …

Generative neural networks are probabilistic models that implement sampling using
feedforward neural networks: they take a random input vector and produce a sample from a …

We propose a framework for analyzing and comparing distributions, which we use to
construct statistical tests to determine if two samples are drawn from different distributions …

Approaches based on deep neural networks have achieved striking performance when
testing data and training data share similar distribution, but can significantly fail otherwise …

We propose two statistical tests to determine if two samples are from different distributions.
Our test statistic is in both cases the distance between the means of the two samples …

Multi-view clustering, which exploits the multi-view information to partition data into their
clusters, has attracted intense attention. However, most existing methods directly learn a …

Abstract Knowledge distillation (KD) has proven to be a highly effective approach for
enhancing model performance through a teacher-student training scheme. However, most …