Jakub M. Tomczak

research

∙ 03/27/2023

Exploring Continual Learning of Diffusion Models

Diffusion models have achieved remarkable success in generating high-qua...

0 Michał Zając, et al. ∙

research

∙ 02/20/2023

Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders

Hierarchical Variational Autoencoders (VAEs) are among the most popular ...

0 Anna Kuzina, et al. ∙

research

∙ 01/31/2023

Learning Data Representations with Joint Diffusion Models

We introduce a joint diffusion model that simultaneously learns meaningf...

0 Kamil Deja, et al. ∙

research

∙ 01/25/2023

Modelling Long Range Dependencies in N-D: From Task-Specific to a General Purpose CNN

Performant Convolutional Neural Network (CNN) architectures must be tail...

0 David M. Knigge, et al. ∙

research

∙ 12/23/2022

A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference

We study the problem of combining neural networks with symbolic reasonin...

0 Emile van Krieken, et al. ∙

research

∙ 06/07/2022

Towards a General Purpose CNN for Long Range Dependencies in ND

The use of Convolutional Neural Networks (CNNs) is widespread in Deep Le...

26 David W. Romero, et al. ∙

research

∙ 05/31/2022

On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models

Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art pe...

0 Kamil Deja, et al. ∙

research

∙ 03/18/2022

Defending Variational Autoencoders from Adversarial Attacks with MCMC

Variational autoencoders (VAEs) are deep generative models used in vario...

0 Anna Kuzina, et al. ∙

research

∙ 10/15/2021

FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes

When designing Convolutional Neural Networks (CNNs), one must select the...

0 David W. Romero, et al. ∙

research

∙ 09/22/2021

Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization

Spiking neural networks are a promising approach towards next-generation...

26 Justus F. Hübotter, et al. ∙

research

∙ 07/17/2021

The Effects of Learning in Morphologically Evolving Robot Systems

When controllers (brains) and morphologies (bodies) of robots simultaneo...

0 Jie Luo, et al. ∙

research

∙ 04/01/2021

Storchastic: A Framework for General Stochastic Automatic Differentiation

Modelers use automatic differentiation of computation graphs to implemen...

0 Emile van Krieken, et al. ∙

research

∙ 03/10/2021

Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks

In this work, we explore adversarial attacks on the Variational Autoenco...

0 Anna Kuzina, et al. ∙

research

∙ 02/04/2021

Invertible DenseNets with Concatenated LipSwish

We introduce Invertible Dense Networks (i-DenseNets), a more parameter e...

5 Yura Perugachi-Diaz, et al. ∙

research

∙ 02/04/2021

CKConv: Continuous Kernel Convolution For Sequential Data

Conventional neural architectures for sequential data present important ...

0 David W. Romero, et al. ∙

research

∙ 11/30/2020

General Invertible Transformations for Flow-based Generative Modeling

In this paper, we present a new class of invertible transformations. We ...

0 Jakub M. Tomczak, et al. ∙

research

∙ 10/19/2020

ABC-Di: Approximate Bayesian Computation for Discrete Data

Many real-life problems are represented as a black-box, i.e., the intern...

0 Ilze Amanda Auzina, et al. ∙

research

∙ 10/19/2020

Learning Locomotion Skills in Evolvable Robots

The challenge of robotic reproduction – making of new robots by recombin...

0 Gongjin Lan, et al. ∙

research

∙ 10/05/2020

i-DenseNets

We introduce Invertible Dense Networks (i-DenseNets), a more parameter e...

0 Yura Perugachi-Diaz, et al. ∙

research

∙ 10/05/2020

Self-Supervised Variational Auto-Encoders

Density estimation, compression and data generation are crucial tasks in...

0 Ioannis Gatopoulos, et al. ∙

research

∙ 09/19/2020

Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae

Models in systems biology are mathematical descriptions of biological pr...

0 Ewelina Weglarz-Tomczak, et al. ∙

research

∙ 06/09/2020

Wavelet Networks: Scale Equivariant Learning From Raw Waveforms

Inducing symmetry equivariance in deep neural architectures has resolved...

0 David W. Romero, et al. ∙

research

∙ 06/09/2020

Super-resolution Variational Auto-Encoders

The framework of variational autoencoders (VAEs) provides a principled m...

0 Ioannis Gatopoulos, et al. ∙

research

∙ 06/02/2020

The Convolution Exponential and Generalized Sylvester Flows

This paper introduces a new method to build linear flows, by taking the ...

10 Emiel Hoogeboom, et al. ∙

research

∙ 05/04/2020

Designing Data Augmentation for Simulating Interventions

Machine learning models trained with purely observational data and the p...

49 Maximilian Ilse, et al. ∙

research

∙ 05/04/2020

Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm

Not all generate-and-test search algorithms are created equal. Bayesian ...

0 Gongjin Lan, et al. ∙

research

∙ 02/07/2020

Differential Evolution with Reversible Linear Transformations

Differential evolution (DE) is a well-known type of evolutionary algorit...

0 Jakub M. Tomczak, et al. ∙

research

∙ 02/07/2020

Attentive Group Equivariant Convolutional Networks

Although group convolutional networks are able to learn powerful represe...

14 David W. Romero, et al. ∙

research

∙ 01/30/2020

Learning Discrete Distributions by Dequantization

Media is generally stored digitally and is therefore discrete. Many succ...

16 Emiel Hoogeboom, et al. ∙

research

∙ 01/21/2020

Learning Directed Locomotion in Modular Robots with Evolvable Morphologies

We generalize the well-studied problem of gait learning in modular robot...

5 Gongjin Lan, et al. ∙

research

∙ 10/07/2019

Increasing Expressivity of a Hyperspherical VAE

Learning suitable latent representations for observed, high-dimensional ...

32 Tim R. Davidson, et al. ∙

research

∙ 08/14/2019

Video Compression With Rate-Distortion Autoencoders

In this paper we present a a deep generative model for lossy video compr...

7 Amirhossein Habibian, et al. ∙

research

∙ 05/24/2019

DIVA: Domain Invariant Variational Autoencoders

We consider the problem of domain generalization, namely, how to learn r...

11 Maximilian Ilse, et al. ∙

research

∙ 04/26/2019

Simulating Execution Time of Tensor Programs using Graph Neural Networks

Optimizing the execution time of tensor program, e.g., a convolution, in...

0 Jakub M. Tomczak, et al. ∙

research

∙ 02/01/2019

Combinatorial Bayesian Optimization using Graph Representations

This paper focuses on Bayesian Optimization - typically considered with ...

2 ChangYong Oh, et al. ∙

research

∙ 06/26/2018

Hierarchical VampPrior Variational Fair Auto-Encoder

Decision making is a process that is extremely prone to different biases...

0 Philip Botros, et al. ∙

research

∙ 04/03/2018

Hyperspherical Variational Auto-Encoders

The Variational Auto-Encoder (VAE) is one of the most used unsupervised ...

0 Tim R. Davidson, et al. ∙

research

∙ 03/15/2018

Sylvester Normalizing Flows for Variational Inference

Variational inference relies on flexible approximate posterior distribut...

0 Rianne van den Berg, et al. ∙

research

∙ 02/13/2018

Attention-based Deep Multiple Instance Learning

Multiple instance learning (MIL) is a variation of supervised learning w...

0 Maximilian Ilse, et al. ∙

research

∙ 12/01/2017

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

The computer-aided analysis of medical scans is a longstanding goal in t...

0 Jakub M. Tomczak, et al. ∙

research

∙ 06/07/2017

Improving Variational Auto-Encoders using convex combination linear Inverse Autoregressive Flow

In this paper, we propose a new volume-preserving flow and show that it ...

0 Jakub M. Tomczak, et al. ∙

research

∙ 05/19/2017

VAE with a VampPrior

Many different methods to train deep generative models have been introdu...

0 Jakub M. Tomczak, et al. ∙

research

∙ 10/23/2016

Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening

We introduce a deep learning architecture for structure-based virtual sc...

0 Adam Gonczarek, et al. ∙

Jakub M. Tomczak

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