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Journal Articles
- 2024
[j3]Valeriya Khan
, Sebastian Cygert, Kamil Deja, Tomasz Trzcinski, Bartlomiej Twardowski:
Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning. IEEE Access 12: 45309-45317 (2024)- 2022
- [j2]Wojciech Masarczyk, Pawel Wawrzynski, Daniel Marczak, Kamil Deja, Tomasz Trzcinski:
Logarithmic Continual Learning. IEEE Access 10: 117001-117010 (2022) - 2021
- [j1]Kamil Deja, Jan Dubinski, Piotr Nowak, Sandro Wenzel, Przemyslaw Spurek, Tomasz Trzcinski:
End-to-End Sinkhorn Autoencoder With Noise Generator. IEEE Access 9: 7211-7219 (2021)
Conference and Workshop Papers
- 2023
- [c9]Georgi Tinchev, Marta Czarnowska, Kamil Deja, Kayoko Yanagisawa, Marius Cotescu:
Modelling Low-Resource Accents Without Accent-Specific TTS Frontend. ICASSP 2023: 1-5 - [c8]Kamil Deja, Georgi Tinchev, Marta Czarnowska, Marius Cotescu, Jasha Droppo:
Diffusion-based accent modelling in speech synthesis. INTERSPEECH 2023: 5516-5520 - [c7]Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak:
Learning Data Representations with Joint Diffusion Models. ECML/PKDD (2) 2023: 543-559 - 2022
- [c6]Jan Dubinski, Kamil Deja, Sandro Wenzel, Przemyslaw Rokita, Tomasz Trzcinski:
Selectively Increasing the Diversity of GAN-Generated Samples. ICONIP (1) 2022: 260-270 - [c5]Kamil Deja, Pawel Wawrzynski, Wojciech Masarczyk, Daniel Marczak, Tomasz Trzcinski:
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning. IJCAI 2022: 2902-2908 - [c4]Kamil Deja, Ariadna Sánchez, Julian Roth, Marius Cotescu:
Automatic Evaluation of Speaker Similarity. INTERSPEECH 2022: 2348-2352 - [c3]Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak:
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. NeurIPS 2022 - 2021
- [c2]Wojciech Masarczyk, Kamil Deja, Tomasz Trzcinski:
On Robustness of Generative Representations Against Catastrophic Forgetting. ICONIP (6) 2021: 325-333 - [c1]Kamil Deja, Pawel Wawrzynski, Daniel Marczak, Wojciech Masarczyk, Tomasz Trzcinski:
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning. IJCNN 2021: 1-8
Informal and Other Publications
- 2024
- [i21]Milosz Kasak, Kamil Deja, Maja Karwowska, Monika Jakubowska, Lukasz Graczykowski, Malgorzata Janik:
Machine-learning-based particle identification with missing data. CoRR abs/2401.01905 (2024) - [i20]Bartosz Cywinski, Kamil Deja, Tomasz Trzcinski, Bartlomiej Twardowski, Lukasz Kucinski:
GUIDE: Guidance-based Incremental Learning with Diffusion Models. CoRR abs/2403.03938 (2024) - [i19]Maja Karwowska, Lukasz Graczykowski, Kamil Deja, Milosz Kasak, Malgorzata Janik:
Particle identification with machine learning from incomplete data in the ALICE experiment. CoRR abs/2403.17436 (2024) - [i18]Karol Rogozinski, Jan Dubinski, Przemyslaw Rokita, Kamil Deja:
Particle physics DL-simulation with control over generated data properties. CoRR abs/2405.14049 (2024) - [i17]Mikolaj Kita, Jan Dubinski, Przemyslaw Rokita, Kamil Deja:
Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN. CoRR abs/2406.03233 (2024) - [i16]Patryk Bedkowski, Jan Dubinski, Kamil Deja, Przemyslaw Rokita:
Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN. CoRR abs/2406.03263 (2024) - 2023
- [i15]Georgi Tinchev, Marta Czarnowska, Kamil Deja, Kayoko Yanagisawa, Marius Cotescu:
Modelling low-resource accents without accent-specific TTS frontend. CoRR abs/2301.04606 (2023) - [i14]Kamil Deja, Tomasz Trzcinski, Jakub M. Tomczak:
Learning Data Representations with Joint Diffusion Models. CoRR abs/2301.13622 (2023) - [i13]Michal Zajac, Kamil Deja, Anna Kuzina, Jakub M. Tomczak, Tomasz Trzcinski, Florian Shkurti, Piotr Milos:
Exploring Continual Learning of Diffusion Models. CoRR abs/2303.15342 (2023) - [i12]Jan Dubinski, Kamil Deja, Sandro Wenzel, Przemyslaw Rokita, Tomasz Trzcinski:
Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN. CoRR abs/2306.13606 (2023) - [i11]Valeriya Khan, Sebastian Cygert, Kamil Deja, Tomasz Trzcinski, Bartlomiej Twardowski:
Looking through the past: better knowledge retention for generative replay in continual learning. CoRR abs/2309.10012 (2023) - [i10]Mateusz Pyla, Kamil Deja, Bartlomiej Twardowski, Tomasz Trzcinski:
Bayesian Flow Networks in Continual Learning. CoRR abs/2310.12001 (2023) - [i9]Kamil Deja, Bartosz Cywinski, Jan Rybarczyk, Tomasz Trzcinski:
Adapt & Align: Continual Learning with Generative Models Latent Space Alignment. CoRR abs/2312.13699 (2023) - 2022
- [i8]Wojciech Masarczyk, Pawel Wawrzynski, Daniel Marczak, Kamil Deja, Tomasz Trzcinski:
Logarithmic Continual Learning. CoRR abs/2201.06534 (2022) - [i7]Kamil Deja, Anna Kuzina, Tomasz Trzcinski, Jakub M. Tomczak:
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. CoRR abs/2206.00070 (2022) - [i6]Kamil Deja, Ariadna Sánchez, Julian Roth, Marius Cotescu:
Automatic Evaluation of Speaker Similarity. CoRR abs/2207.00344 (2022) - [i5]Jan Dubinski, Kamil Deja, Sandro Wenzel, Przemyslaw Rokita, Tomasz Trzcinski:
Selectively increasing the diversity of GAN-generated samples. CoRR abs/2207.01561 (2022) - 2021
- [i4]Kamil Deja, Pawel Wawrzynski, Daniel Marczak, Wojciech Masarczyk, Tomasz Trzcinski:
Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning. CoRR abs/2106.12196 (2021) - [i3]Wojciech Masarczyk, Kamil Deja, Tomasz Trzcinski:
On robustness of generative representations against catastrophic forgetting. CoRR abs/2109.01844 (2021) - 2020
- [i2]Kamil Deja, Jan Dubinski, Piotr Nowak, Sandro Wenzel, Tomasz Trzcinski:
End-to-end Sinkhorn Autoencoder with Noise Generator. CoRR abs/2006.06704 (2020) - [i1]Kamil Deja, Pawel Wawrzynski, Daniel Marczak, Wojciech Masarczyk, Tomasz Trzcinski:
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning. CoRR abs/2011.14960 (2020)
Coauthor Index
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last updated on 2024-08-07 22:31 CEST by the dblp team
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