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Kamil Deja
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2020 – today
- 2024
- [j3]Valeriya Khan
, Sebastian Cygert
Looking Through the Past: Better Knowledge Retention for Generative Replay in Continual Learning. IEEE Access 12: 45309-45317 (2024) - [i23]Milosz Kasak, Kamil Deja
Machine-learning-based particle identification with missing data. CoRR abs/2401.01905 (2024) - [i22]Bartosz Cywinski, Kamil Deja
GUIDE: Guidance-based Incremental Learning with Diffusion Models. CoRR abs/2403.03938 (2024) - [i21]Maja Karwowska, Lukasz Graczykowski, Kamil Deja
Particle identification with machine learning from incomplete data in the ALICE experiment. CoRR abs/2403.17436 (2024) - [i20]Karol Rogozinski, Jan Dubinski, Przemyslaw Rokita, Kamil Deja
Particle physics DL-simulation with control over generated data properties. CoRR abs/2405.14049 (2024) - [i19]Mikolaj Kita, Jan Dubinski, Przemyslaw Rokita, Kamil Deja:
Generative Diffusion Models for Fast Simulations of Particle Collisions at CERN. CoRR abs/2406.03233 (2024) - [i18]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) - [i17]Lukasz Staniszewski, Katarzyna Zaleska, Kamil Deja:
Low-Rank Continual Personalization of Diffusion Models. CoRR abs/2410.04891 (2024) - [i16]Lukasz Staniszewski, Lukasz Kucinski, Kamil Deja:
There and Back Again: On the relation between noises, images, and their inversions in diffusion models. CoRR abs/2410.23530 (2024) - 2023
- [c9]Georgi Tinchev, Marta Czarnowska, Kamil Deja
Modelling Low-Resource Accents Without Accent-Specific TTS Frontend. ICASSP 2023: 1-5 - [c8]Kamil Deja
Diffusion-based accent modelling in speech synthesis. INTERSPEECH 2023: 5516-5520 - [c7]Kamil Deja
Learning Data Representations with Joint Diffusion Models. ECML/PKDD (2) 2023: 543-559 - [i15]Georgi Tinchev, Marta Czarnowska, Kamil Deja
Modelling low-resource accents without accent-specific TTS frontend. CoRR abs/2301.04606 (2023) - [i14]Kamil Deja
Learning Data Representations with Joint Diffusion Models. CoRR abs/2301.13622 (2023) - [i13]Michal Zajac, Kamil Deja
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
Looking through the past: better knowledge retention for generative replay in continual learning. CoRR abs/2309.10012 (2023) - [i10]Mateusz Pyla, Kamil Deja
Bayesian Flow Networks in Continual Learning. CoRR abs/2310.12001 (2023) - [i9]Kamil Deja
Adapt & Align: Continual Learning with Generative Models Latent Space Alignment. CoRR abs/2312.13699 (2023) - 2022
- [j2]Wojciech Masarczyk
Logarithmic Continual Learning. IEEE Access 10: 117001-117010 (2022) - [c6]Jan Dubinski
Selectively Increasing the Diversity of GAN-Generated Samples. ICONIP (1) 2022: 260-270 - [c5]Kamil Deja, Pawel Wawrzynski, Wojciech Masarczyk, Daniel Marczak
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning. IJCAI 2022: 2902-2908 - [c4]Kamil Deja
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 - [i8]Wojciech Masarczyk, Pawel Wawrzynski, Daniel Marczak, Kamil Deja, Tomasz Trzcinski:
Logarithmic Continual Learning. CoRR abs/2201.06534 (2022) - [i7]Kamil Deja
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models. CoRR abs/2206.00070 (2022) - [i6]Kamil Deja
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
- [j1]Kamil Deja
End-to-End Sinkhorn Autoencoder With Noise Generator. IEEE Access 9: 7211-7219 (2021) - [c2]Wojciech Masarczyk
On Robustness of Generative Representations Against Catastrophic Forgetting. ICONIP (6) 2021: 325-333 - [c1]Kamil Deja
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning. IJCNN 2021: 1-8 - [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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