675 citations · 1.8k across the 55 of their papers we have counts for
14 papers · 1 filter
Storchastic: A Framework for General Stochastic Automatic Differentiation
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
Modelers use automatic differentiation (AD) of computation graphs to implement complex Deep Learning models without defining gradient computations. Stochastic AD extends AD to stoc…
Invertible DenseNets with Concatenated LipSwish
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient extension of Residual Flows. The method relies on an analysis of the Lipschitz continuity of the co…
ABC-Di: Approximate Bayesian Computation for Discrete Data
Ilze Amanda Auzina, Jakub M. Tomczak
Many real-life problems are represented as a black-box, i.e., the internal workings are inaccessible or a closed-form mathematical expression of the likelihood function cannot be d…
Self-Supervised Variational Auto-Encoders
Ioannis Gatopoulos, Jakub M. Tomczak
Density estimation, compression and data generation are crucial tasks in artificial intelligence. Variational Auto-Encoders (VAEs) constitute a single framework to achieve these go…
Selecting Data Augmentation for Simulating Interventions
Maximilian Ilse, Jakub M. Tomczak, Patrick Forré
Machine learning models trained with purely observational data and the principle of empirical risk minimization \citep{vapnik_principles_1992} can fail to generalize to unseen doma…
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…
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…
Hierarchical VampPrior Variational Fair Auto-Encoder
Philip Botros, Jakub M. Tomczak
Decision making is a process that is extremely prone to different biases. In this paper we consider learning fair representations that aim at removing nuisance (sensitive) informat…
Hyperspherical Variational Auto-Encoders
Tim R. Davidson, Luca Falorsi, Nicola De Cao +2
The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and p…
Sylvester Normalizing Flows for Variational Inference
Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak +1
Variational inference relies on flexible approximate posterior distributions. Normalizing flows provide a general recipe to construct flexible variational posteriors. We introduce…
Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow
Jakub M. Tomczak, Max Welling
In this paper, we propose a new volume-preserving flow and show that it performs similarly to the linear general normalizing flow. The idea is to enrich a linear Inverse Autoregres…
Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening
Adam Gonczarek, Jakub M. Tomczak, Szymon Zaręba +3
We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom c…