dr. Jakub Tomczak

dr.

20092019
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Personal profile

Ancillary activities

  • Qualcomm AI Research | Amsterdam | Medewerker | 2019-11-01 - present

Ancillary activities are updated daily

Fingerprint Dive into the research topics where Jakub Tomczak is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Learning Medicine & Life Sciences
Labels Engineering & Materials Science
Resource allocation Engineering & Materials Science
Probability distributions Engineering & Materials Science
Character recognition Engineering & Materials Science
Support vector machines Engineering & Materials Science
Classifiers Engineering & Materials Science
Experiments Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2009 2019

DIVA: Domain invariant variational autoencoder

Ilse, M., Tomczak, J. M., Louizos, C. & Welling, M., 1 Jan 2019.

Research output: Contribution to ConferencePaperAcademic

Medical imaging
learning
performance
Generative
Benchmark

Estimating kinetic constants in the Michaelis–Menten model from one enzymatic assay using Approximate Bayesian Computation

Tomczak, J. M. & Węglarz-Tomczak, E., 1 Oct 2019, In : FEBS Letters. 593, 19, p. 2742-2750 9 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Open Access
Enzyme Assays
Assays
Enzyme kinetics
Biochemistry
Kinetic parameters

Low-Dimensional Perturb-and-MAP Approach for Learning Restricted Boltzmann Machines

Tomczak, J. M., Zaręba, S., Ravanbakhsh, S. & Greiner, R., 1 Oct 2019, In : Neural Processing Letters. 50, 2, p. 1401-1419 19 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Open Access
Learning
Play and Playthings
Maximum likelihood
Sampling
Datasets

Attention-based deep multiple instance learning

Ilse, M., Tomczak, J. M. & Welling, M., 1 Jan 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 3376-3391 16 p. (35th International Conference on Machine Learning, ICML 2018; vol. 5).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Labels
Neural networks
Supervised learning
Agglomeration

Helix-loop-helix peptide foldamers and their use in the construction of hydrolase mimetics

Drewniak, M., Węglarz-Tomczak, E., Ożga, K., Rudzińska-Szostak, E., Macegoniuk, K., Tomczak, J. M., Bejger, M., Rypniewski, W. & Berlicki, Ł., 1 Dec 2018, In : Bioorganic Chemistry. 81, p. 356-361 6 p.

Research output: Contribution to JournalArticleAcademicpeer-review

Hydrolases
Cycloleucine
Peptides
Enzymes
Nuclear magnetic resonance spectroscopy