675 citations · 1.8k across the 55 of their papers we have counts for
5 papers · 1 filter
Increasing Expressivity of a Hyperspherical VAE
Tim R. Davidson, Jakub M. Tomczak, Efstratios Gavves
Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning. While traditionally…
Video Compression With Rate-Distortion Autoencoders
Amirhossein Habibian, Ties van Rozendaal, Jakub M. Tomczak +1
In this paper we present a a deep generative model for lossy video compression. We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressi…
DIVA: Domain Invariant Variational Autoencoders
Maximilian Ilse, Jakub M. Tomczak, Christos Louizos +1
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…
Simulating Execution Time of Tensor Programs using Graph Neural Networks
Jakub M. Tomczak, Romain Lepert, Auke Wiggers
Optimizing the execution time of tensor program, e.g., a convolution, involves finding its optimal configuration. Searching the configuration space exhaustively is typically infeas…
Combinatorial Bayesian Optimization using the Graph Cartesian Product
Changyong Oh, Jakub M. Tomczak, Efstratios Gavves +1
This paper focuses on Bayesian Optimization (BO) for objectives on combinatorial search spaces, including ordinal and categorical variables. Despite the abundance of potential appl…