Jakub M. Tomczak

Orcid: 0000-0001-8634-694X

According to our database1, Jakub M. Tomczak authored at least 94 papers between 2010 and 2024.

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Bibliography

2024
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series.
Trans. Mach. Learn. Res., 2024

Generative AI Systems: A Systems-based Perspective on Generative AI.
CoRR, 2024

Variational Stochastic Gradient Descent for Deep Neural Networks.
CoRR, 2024

Mixed Models with Multiple Instance Learning.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Deep Generative Modeling, Second Editiontion
Springer, ISBN: 978-3-031-64086-5, 2024

2023
Attention-based Multi-instance Mixed Models.
CoRR, 2023

De Novo Drug Design with Joint Transformers.
CoRR, 2023

Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better.
CoRR, 2023

Exploring Continual Learning of Diffusion Models.
CoRR, 2023

Analyzing the Posterior Collapse in Hierarchical Variational Autoencoders.
CoRR, 2023

A Comparison of Controller Architectures and Learning Mechanisms for Arbitrary Robot Morphologies.
Proceedings of the IEEE Symposium Series on Computational Intelligence, 2023

Learning Data Representations with Joint Diffusion Models.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Continuous Kendall Shape Variational Autoencoders.
Proceedings of the Geometric Science of Information - 6th International Conference, 2023

2022
Time efficiency in optimization with a bayesian-Evolutionary algorithm.
Swarm Evol. Comput., 2022

The Effects of Learning in Morphologically Evolving Robot Systems.
Frontiers Robotics AI, 2022

Towards a General Purpose CNN for Long Range Dependencies in ND.
CoRR, 2022

Defending Variational Autoencoders from Adversarial Attacks with MCMC.
CoRR, 2022

Alleviating Adversarial Attacks on Variational Autoencoders with MCMC.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

CKConv: Continuous Kernel Convolution For Sequential Data.
Proceedings of the Tenth International Conference on Learning Representations, 2022

FlexConv: Continuous Kernel Convolutions With Differentiable Kernel Sizes.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Deep Generative Modeling
Springer, ISBN: 978-3-030-93157-5, 2022

2021
Learning locomotion skills in evolvable robots.
Neurocomputing, 2021

Self-Supervised Variational Auto-Encoders.
Entropy, 2021

Approximate Bayesian Computation for Discrete Spaces.
Entropy, 2021

Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization.
CoRR, 2021

The Effects of Learning in Morphologically Evolving Robot Systems.
CoRR, 2021

Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks.
CoRR, 2021

Deep learning for white cabbage seedling prediction.
Comput. Electron. Agric., 2021

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.
Bioinform., 2021

Learning directed locomotion in modular robots with evolvable morphologies.
Appl. Soft Comput., 2021

Invertible DenseNets with Concatenated LipSwish.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Storchastic: A Framework for General Stochastic Automatic Differentiation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Selecting Data Augmentation for Simulating Interventions.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
General Invertible Transformations for Flow-based Generative Modeling.
CoRR, 2020

ABC-Di: Approximate Bayesian Computation for Discrete Data.
CoRR, 2020

Population-based Optimization for Kinetic Parameter Identification in Glycolytic Pathway in Saccharomyces cerevisiae.
CoRR, 2020

i-DenseNets.
CoRR, 2020

Wavelet Networks: Scale Equivariant Learning From Raw Waveforms.
CoRR, 2020

Super-resolution Variational Auto-Encoders.
CoRR, 2020

Designing Data Augmentation for Simulating Interventions.
CoRR, 2020

Learning Discrete Distributions by Dequantization.
CoRR, 2020

The Convolution Exponential and Generalized Sylvester Flows.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generative Fourier-Based Auto-encoders: Preliminary Results.
Proceedings of the Machine Learning, Optimization, and Data Science, 2020

DIVA: Domain Invariant Variational Autoencoders.
Proceedings of the International Conference on Medical Imaging with Deep Learning, 2020

Attentive Group Equivariant Convolutional Networks.
Proceedings of the 37th International Conference on Machine Learning, 2020

Differential Evolution with Reversible Linear Transformations.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization.
Proceedings of the IEEE Congress on Evolutionary Computation, 2020

2019
Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines.
Neural Process. Lett., 2019

Increasing Expressivity of a Hyperspherical VAE.
CoRR, 2019

Simulating Execution Time of Tensor Programs using Graph Neural Networks.
CoRR, 2019

Combinatorial Bayesian Optimization using Graph Representations.
CoRR, 2019

Combinatorial Bayesian Optimization using the Graph Cartesian Product.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

DIVA: Domain Invariant Variational Autoencoder.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

Video Compression With Rate-Distortion Autoencoders.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018
Hierarchical VampPrior Variational Fair Auto-Encoder.
CoRR, 2018

Interaction prediction in structure-based virtual screening using deep learning.
Comput. Biol. Medicine, 2018

Hyperspherical Variational Auto-Encoders.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Sylvester Normalizing Flows for Variational Inference.
Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence, 2018

Attention-based Deep Multiple Instance Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

VAE with a VampPrior.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Learning Invariant Features Using Subspace Restricted Boltzmann Machine.
Neural Process. Lett., 2017

Deep Learning with Permutation-invariant Operator for Multi-instance Histopathology Classification.
CoRR, 2017

2016
Learning Informative Features from Restricted Boltzmann Machines.
Neural Process. Lett., 2016

Articulated tracking with manifold regularized particle filter.
Mach. Vis. Appl., 2016

Ensemble boosted trees with synthetic features generation in application to bankruptcy prediction.
Expert Syst. Appl., 2016

Improving Variational Auto-Encoders using Householder Flow.
CoRR, 2016

Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening.
CoRR, 2016

Self-paced Learning for Imbalanced Data.
Proceedings of the Intelligent Information and Database Systems - 8th Asian Conference, 2016

2015
Boosted SVM with active learning strategy for imbalanced data.
Soft Comput., 2015

Probabilistic combination of classification rules and its application to medical diagnosis.
Mach. Learn., 2015

Classification Restricted Boltzmann Machine for comprehensible credit scoring model.
Expert Syst. Appl., 2015

Improving neural networks with bunches of neurons modeled by Kumaraswamy units: Preliminary study.
CoRR, 2015

RBM-SMOTE: Restricted Boltzmann Machines for Synthetic Minority Oversampling Technique.
Proceedings of the Intelligent Information and Database Systems - 7th Asian Conference, 2015

2014
Subspace Restricted Boltzmann Machine.
CoRR, 2014

Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients.
Appl. Soft Comput., 2014

Selecting right questions with Restricted Boltzmann Machines.
Proceedings of the Progress in Systems Engineering, 2014

Accelerated learning for Restricted Boltzmann Machine with momentum term.
Proceedings of the Progress in Systems Engineering, 2014

Sparse hidden units activation in Restricted Boltzmann Machine.
Proceedings of the Progress in Systems Engineering, 2014

2013
Decision rules extraction from data stream in the presence of changing context for diabetes treatment.
Knowl. Inf. Syst., 2013

Prediction of breast cancer recurrence using Classification Restricted Boltzmann Machine with Dropping.
CoRR, 2013

On-line bayesian context change detection in web service systems.
Proceedings of the 2013 international workshop on Hot topics in cloud services, 2013

Associative Learning Using Ising-Like Model.
Proceedings of the Advances in Systems Science, 2013

Manifold Regularized Particle Filter for Articulated Human Motion Tracking.
Proceedings of the Advances in Systems Science, 2013

2012
Gaussian process regression as a predictive model for Quality-of-Service in Web service systems
CoRR, 2012

On-Line Change Detection for Resource Allocation in Service-Oriented Systems.
Proceedings of the Technological Innovation for Value Creation, 2012

Development of Service Composition by Applying ICT Service Mapping.
Proceedings of the Computer Networks - 19th International Conference, 2012

A Probabilistic Approach to Structural Change Prediction in Evolving Social Networks.
Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, 2012

2011
Context Change Detection for Resource Allocation in Service-Oriented Systems.
Proceedings of the Knowlege-Based and Intelligent Information and Engineering Systems, 2011

Personalisation in Service-Oriented Systems Using Markov Chain Model and Bayesian Inference.
Proceedings of the Technological Innovation for Sustainability, 2011

2010
Service Discovery Approach Based on Rough Sets for SOA Systems.
Proceedings of the Advances in Multimedia and Network Information System Technologies, 2010

Student Courses Recommendation Using Ant Colony Optimization.
Proceedings of the Intelligent Information and Database Systems, 2010


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