Tomczak, Jakub M.

14 publications

TMLR 2026 Synergistic Benefits of Joint Molecule Generation and Property Prediction Adam Izdebski, Jan Olszewski, Pankhil Gawade, Krzysztof Koras, Serra Korkmaz, Valentin Rauscher, Jakub M. Tomczak, Ewa Szczurek

TMLR 2025 Variational Stochastic Gradient Descent for Deep Neural Networks Anna Kuzina, Haotian Chen, Babak Esmaeili, Jakub M. Tomczak

TMLR 2024 Hierarchical VAE with a Diffusion-Based VampPrior Anna Kuzina, Jakub M. Tomczak

NeurIPSW 2024 Hypernetworks for Image Recontextualization Maciej Zieba, Jakub Balicki, Tomasz Drozdz, Konrad Karanowski, Pawel Lorek, Hong Lyu, Aleksander Piotr Skorupa, Tomasz Trzcinski, Oriol Caudevilla, Jakub M. Tomczak

AISTATS 2024 Mixed Models with Multiple Instance Learning Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian Theis, Francesco Paolo Casale

ICMLW 2024 Variational Stochastic Gradient Descent for Deep Neural Networks Haotian Chen, Anna Kuzina, Babak Esmaeili, Jakub M. Tomczak

TMLR 2024 Wavelet Networks: Scale-Translation Equivariant Learning from Raw Time-Series David W. Romero, Erik J Bekkers, Jakub M. Tomczak, Mark Hoogendoorn

ECML-PKDD 2023 Learning Data Representations with Joint Diffusion Models Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak

ICML 2021 Selecting Data Augmentation for Simulating Interventions Maximilian Ilse, Jakub M Tomczak, Patrick Forré

ICLRW 2019 DIVA: Domain Invariant Variational Autoencoder Maximilian Ilse, Jakub M. Tomczak, Christos Louizos, Max Welling

UAI 2018 Hyperspherical Variational Auto-Encoders Tim R. Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, Jakub M. Tomczak

UAI 2018 Sylvester Normalizing Flows for Variational Inference Rianne van den Berg, Leonard Hasenclever, Jakub M. Tomczak, Max Welling

AISTATS 2018 VAE with a VampPrior Jakub M. Tomczak, Max Welling

MLJ 2015 Probabilistic Combination of Classification Rules and Its Application to Medical Diagnosis Jakub M. Tomczak, Maciej Zieba