Max Welling

Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification

2017 · arXiv (Cornell University)

The computer-aided analysis of medical scans is a longstanding goal in the medical imaging field. Currently, deep learning has became a dominant methodology for supporting pathologists and radiologist. Deep learning algorithms have been successfully applied …

DIVA: Domain Invariant Variational Autoencoder.

2019

We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the domain invariant VAE (DIVA), …

Variational Bayes in Private Settings (VIPS) (Extended Abstract)

2020

Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a …

Mixed Variable Bayesian Optimization with Frequency Modulated Kernels

2021 · arXiv (Cornell University)

The sample efficiency of Bayesian optimization(BO) is often boosted by Gaussian Process(GP) surrogate models. However, on mixed variable spaces, surrogate models other than GPs are prevalent, mainly due to the lack of kernels which can …

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

2021 · arXiv (Cornell University)

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds. Existing work has primarily focused on a small number of groups, such as the translation, …