
ABCDi: Approximate Bayesian Computation for Discrete Data
Many reallife problems are represented as a blackbox, i.e., the intern...
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Learning Locomotion Skills in Evolvable Robots
The challenge of robotic reproduction – making of new robots by recombin...
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iDenseNets
We introduce Invertible Dense Networks (iDenseNets), a more parameter e...
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SelfSupervised Variational AutoEncoders
Density estimation, compression and data generation are crucial tasks in...
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Populationbased Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae
Models in systems biology are mathematical descriptions of biological pr...
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Wavelet Networks: Scale Equivariant Learning From Raw Waveforms
Inducing symmetry equivariance in deep neural architectures has resolved...
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Superresolution Variational AutoEncoders
The framework of variational autoencoders (VAEs) provides a principled m...
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The Convolution Exponential and Generalized Sylvester Flows
This paper introduces a new method to build linear flows, by taking the ...
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Designing Data Augmentation for Simulating Interventions
Machine learning models trained with purely observational data and the p...
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Time Efficiency in Optimization with a BayesianEvolutionary Algorithm
Not all generateandtest search algorithms are created equal. Bayesian ...
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Differential Evolution with Reversible Linear Transformations
Differential evolution (DE) is a wellknown type of evolutionary algorit...
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Attentive Group Equivariant Convolutional Networks
Although group convolutional networks are able to learn powerful represe...
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Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete. Many succ...
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Learning Directed Locomotion in Modular Robots with Evolvable Morphologies
We generalize the wellstudied problem of gait learning in modular robot...
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Increasing Expressivity of a Hyperspherical VAE
Learning suitable latent representations for observed, highdimensional ...
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Video Compression With RateDistortion Autoencoders
In this paper we present a a deep generative model for lossy video compr...
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DIVA: Domain Invariant Variational Autoencoders
We consider the problem of domain generalization, namely, how to learn r...
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Simulating Execution Time of Tensor Programs using Graph Neural Networks
Optimizing the execution time of tensor program, e.g., a convolution, in...
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Combinatorial Bayesian Optimization using Graph Representations
This paper focuses on Bayesian Optimization  typically considered with ...
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Hierarchical VampPrior Variational Fair AutoEncoder
Decision making is a process that is extremely prone to different biases...
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Hyperspherical Variational AutoEncoders
The Variational AutoEncoder (VAE) is one of the most used unsupervised ...
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Sylvester Normalizing Flows for Variational Inference
Variational inference relies on flexible approximate posterior distribut...
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Attentionbased Deep Multiple Instance Learning
Multiple instance learning (MIL) is a variation of supervised learning w...
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Deep Learning with Permutationinvariant Operator for Multiinstance Histopathology Classification
The computeraided analysis of medical scans is a longstanding goal in t...
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Improving Variational AutoEncoders using convex combination linear Inverse Autoregressive Flow
In this paper, we propose a new volumepreserving flow and show that it ...
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VAE with a VampPrior
Many different methods to train deep generative models have been introdu...
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Learning Deep Architectures for Interaction Prediction in Structurebased Virtual Screening
We introduce a deep learning architecture for structurebased virtual sc...
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Jakub M. Tomczak
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