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Igor T. Podolak
Person information
- affiliation: Jagiellonian University, Poland
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2020 – today
- 2024
- [j12]Lukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gainski, Jacek Tabor, Igor T. Podolak, Pawel M. Morkisz, Stanislaw Jastrzebski:
Relative molecule self-attention transformer. J. Cheminformatics 16(1): 3 (2024) - 2023
- [j11]Bartosz Wójcik
, Marcin Przewiezlikowski
, Filip Szatkowski
, Maciej Wolczyk
, Klaudia Balazy, Bartlomiej Krzepkowski, Igor T. Podolak
, Jacek Tabor
, Marek Smieja
, Tomasz Trzcinski
:
Zero time waste in pre-trained early exit neural networks. Neural Networks 168: 580-601 (2023) - [i9]Tomasz Danel, Jan Leski, Sabina Podlewska, Igor T. Podolak:
Docking-based generative approaches in the search for new drug candidates. CoRR abs/2312.13944 (2023) - 2022
- [j10]Szymon Knop, Marcin Mazur, Przemyslaw Spurek, Jacek Tabor, Igor T. Podolak:
Generative models with kernel distance in data space. Neurocomputing 487: 119-129 (2022) - [c16]Szymon Knop, Przemyslaw Spurek, Marcin Mazur, Jacek Tabor, Igor T. Podolak:
Batch Size Reconstruction-Distribution Trade-Off In Kernel Based Generative Autoencoders. ICIP 2022: 3728-3732 - 2021
- [j9]Stefan Mordalski
, Agnieszka Wojtuch
, Igor T. Podolak, Rafal Kurczab, Andrzej J. Bojarski
:
2D SIFt: a matrix of ligand-receptor interactions. J. Cheminformatics 13(1): 66 (2021) - [c15]Maciej Wolczyk, Bartosz Wójcik, Klaudia Balazy, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski:
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks. NeurIPS 2021: 2516-2528 - [i8]Maciej Wolczyk, Bartosz Wójcik, Klaudia Balazy, Igor T. Podolak, Jacek Tabor, Marek Smieja, Tomasz Trzcinski:
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks. CoRR abs/2106.05409 (2021) - [i7]Lukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gainski, Jacek Tabor, Igor T. Podolak, Pawel M. Morkisz, Stanislaw Jastrzebski:
Relative Molecule Self-Attention Transformer. CoRR abs/2110.05841 (2021) - 2020
- [j8]Szymon Knop, Przemyslaw Spurek, Jacek Tabor, Igor T. Podolak, Marcin Mazur, Stanislaw Jastrzebski:
Cramer-Wold Auto-Encoder. J. Mach. Learn. Res. 21: 164:1-164:28 (2020) - [i6]Szymon Knop, Marcin Mazur, Przemyslaw Spurek, Jacek Tabor, Igor T. Podolak:
Generative models with kernel distance in data space. CoRR abs/2009.07327 (2020)
2010 – 2019
- 2019
- [c14]Damian Lesniak, Igor Sieradzki, Igor T. Podolak:
Distribution-Interpolation Trade off in Generative Models. ICLR (Poster) 2019 - [c13]Igor T. Podolak, Adam Roman, Arkadiusz Czekajski, Damian Lesniak, Bartosz Zielinski:
Variational auto-encoders for generating feature-preserving automata. NCMA 2019: 171-185 - [i5]Szymon Knop, Marcin Mazur, Jacek Tabor, Igor T. Podolak, Przemyslaw Spurek:
Sliced generative models. CoRR abs/1901.10417 (2019) - [i4]Lukasz Struski, Jacek Tabor, Igor T. Podolak, Aleksandra Nowak
:
Interpolation in generative models. CoRR abs/1904.03445 (2019) - [i3]Przemyslaw Spurek, Szymon Knop, Jacek Tabor, Igor T. Podolak, Bartosz Wójcik:
One-element Batch Training by Moving Window. CoRR abs/1905.12947 (2019) - 2018
- [j7]Igor T. Podolak, Adam Roman, Marek Szykula
, Bartosz Zielinski:
A machine learning approach to synchronization of automata. Expert Syst. Appl. 97: 357-371 (2018) - [i2]Jacek Tabor, Szymon Knop, Przemyslaw Spurek, Igor T. Podolak, Marcin Mazur, Stanislaw Jastrzebski:
Cramer-Wold AutoEncoder. CoRR abs/1805.09235 (2018) - [i1]Damian Lesniak, Igor Sieradzki, Igor T. Podolak:
On Latent Distributions Without Finite Mean in Generative Models. CoRR abs/1806.01670 (2018) - 2014
- [c12]Wojciech M. Czarnecki
, Igor T. Podolak:
Adaptive Active Learning as a Multi-armed Bandit Problem. ECAI 2014: 989-990 - 2013
- [j6]Igor T. Podolak, Adam Roman:
Theoretical Foundations and Experimental Results for a Hierarchical Classifier with Overlapping Clusters. Comput. Intell. 29(2): 357-388 (2013) - [c11]Wojciech M. Czarnecki
, Igor T. Podolak:
Machine Learning with Known Input Data Uncertainty Measure. CISIM 2013: 379-388 - [c10]Igor T. Podolak, Stanislaw K. Jastrzebski:
Density Invariant Detection of Osteoporosis Using Growing Neural Gas. CORES 2013: 629-638 - 2012
- [c9]Igor T. Podolak, Adam Roman, Dariusz Jedrzejczyk:
Application of Hierarchical Classifier to Minimal Synchronizing Word Problem. ICAISC (1) 2012: 421-429 - 2011
- [j5]Stanislaw Brodowski, Igor T. Podolak:
Hierarchical Estimator. Expert Syst. Appl. 38(10): 12237-12248 (2011) - [j4]Igor T. Podolak, Adam Roman:
CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem. Pattern Anal. Appl. 14(4): 395-413 (2011) - [j3]Igor T. Podolak, Adam Roman:
Risk function estimation for subproblems in a hierarchical classifier. Pattern Recognit. Lett. 32(15): 2136-2142 (2011) - [c8]Igor T. Podolak, Adam Roman:
Risk Estimation for Hierarchical Classifier. HAIS (1) 2011: 156-163 - [c7]Kamil Bartocha, Igor T. Podolak:
Classifier Ensembles for Virtual Concept Drift - The DEnBoost Algorithm. HAIS (2) 2011: 164-171
2000 – 2009
- 2009
- [c6]Igor T. Podolak, Kamil Bartocha:
A Hierarchical Classifier with Growing Neural Gas Clustering. ICANNGA 2009: 283-292 - [p1]Igor T. Podolak, Adam Roman:
A New Notion of Weakness in Classification Theory. Computer Recognition Systems 3 2009: 239-245 - 2008
- [j2]Igor T. Podolak:
Hierarchical classifier with overlapping class groups. Expert Syst. Appl. 34(1): 673-682 (2008) - [c5]Piotr Kalita
, Igor T. Podolak, Adam Roman, Bartosz Bierkowski:
Algorithm for Intelligent Prediction of Requests in Business Systems. SOFSEM 2008: 696-707 - 2007
- [c4]Igor T. Podolak:
Hierarchical Rules for a Hierarchical Classifier. ICANNGA (1) 2007: 749-757 - 2005
- [c3]Igor T. Podolak, Slawomir Biel, Marcin Bobrowski:
Hierarchical Classifier. PPAM 2005: 591-598 - 2002
- [c2]Dihua Xi, Igor T. Podolak, Seong-Whan Lee:
Facial Component Extraction and Face Recognition with Support Vector Machines. FGR 2002: 83-88 - 2000
- [c1]Igor T. Podolak, Seong-Whan Lee, Andrzej Bielecki, Elzbieta Majkut:
A Hybrid Neural System for Phonematic Transformation. ICPR 2000: 2957-2960
1990 – 1999
- 1998
- [j1]Igor T. Podolak:
Functional graph model of a neural network. IEEE Trans. Syst. Man Cybern. Part B 28(6): 876-881 (1998)
Coauthor Index
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last updated on 2024-06-10 20:31 CEST by the dblp team
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