Meta-learning in Deep Neural Networks
|
Jacek Tabor
|
OPUS 25 |
2023 |
grant |
Where to look next - guiding active visual exploration with internal model uncertainty
|
Adam Pardyl
|
PRELUDIUM 22 |
2023 |
grant |
Improving the transferability of self-supervised learning models
|
Marcin Przewięźlikowski
|
PRELUDIUM 22 |
2023 |
grant |
Computationally efficient dynamic neural networks
|
Bartosz Wójcik
|
PRELUDIUM 22 |
2023 |
grant |
Deep generative models in image processing
|
Szymon Knop
|
|
2023 |
PhD defense |
Deep learning methods in pharmaceutical sciences
|
Tomasz Danel
|
|
2023 |
PhD defense |
Zero time waste in pre-trained early exit neural networks
|
Bartosz Wójcik
,
Marcin Przewięźlikowski
,
Filip Szatkowski
,
Maciej Wołczyk
,
Klaudia Bałazy
,
Bartłomiej Krzepkowski
,
Igor Podolak
,
Jacek Tabor
,
Marek Śmieja
,
Tomasz Trzciński
|
Neural Networks, vol. 168 |
2023 |
paper |
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
|
Aleksandra Nowak
,
Bram Grooten
,
Decebal Constantin Mocanu
,
Jacek Tabor
|
NeurIPS 2023 |
2023 |
paper |
Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery
|
Mateusz Olko
,
Michał Zając
,
Aleksandra Nowak
,
Nino Scherrer
,
Yashas Annadani
,
Stefan Bauer
,
Łukasz Kuciński
,
Piotr Miłoś
|
NeurIPS 2023 |
2023 |
paper |
Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders
|
Jan Dubiński
,
Stanisław Pawlak
,
Franzisca Boenisch
,
Tomasz Trzciński
,
Adam Dziedzic
|
NeurIPS 2023 |
|
paper |
The Tunnel Effect: Building Data Representations in Deep Neural Networks
|
Wojciech Masarczyk
,
Mateusz Ostaszewski
,
Ehsan Imani
,
Razvan Pascanu
,
Piotr Miłoś
,
Tomasz Trzciński
|
NeurIPS 2023 |
|
paper |
Document Understanding Dataset and Evaluation (DUDE)
|
Jordy Van Landeghem
,
Rubén Tito
,
Łukasz Borchmann
,
Michał Pietruszka
,
Paweł Józiak
,
Rafał Powalski
,
Dawid Jurkiewicz
,
Mickaël Coustaty
,
Bertrand Ackaert
,
Ernest Valveny
,
Matthew Blaschko
,
Sien Moens
,
Tomasz Stanisławek
|
ICCV 2023 |
2023 |
paper |
ICICLE: Interpretable Class Incremental Continual Learning
|
Dawid Rymarczyk
,
Joost van de Weijer
,
Bartosz Zieliński
,
Bartłomiej Twardowski
|
ICCV 2023 |
2023 |
paper |
CompLung: Comprehensive Computer-Aided Diagnosis of Lung Cancer
|
Adam Pardyl
,
Dawid Rymarczyk
,
Joanna Jaworek-Korjakowska
,
Dariusz Kucharski
,
Andrzej Brodzicki
,
Julia Lasek
,
Zofia Schneider
,
Iwona Kucybała
,
Andrzej Urbanik
,
Rafał Obuchowicz
,
Zbisław Tabor
,
Bartosz Zieliński
|
ECAI 2023 |
2023 |
paper |
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
|
Łukasz Struski
,
Dawid Rymarczyk
,
Arkadiusz Lewicki
,
Robert Sabiniewicz
,
Jacek Tabor
,
Bartosz Zieliński
|
ECAI 2023 |
2023 |
paper |
Hypernetworks build Implicit Neural Representations of Sounds
|
Filip Szatkowski
,
Karol Piczak
,
Przemyslaw Spurek
,
Jacek Tabor
,
Tomasz Trzcinski
|
ECML 2023 |
2023 |
paper |
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
|
Piotr Gaiński
,
Michał Koziarski
,
Jacek Tabor
,
Marek Śmieja
|
ECML 2023 |
2023 |
paper |
Contrastive Hierarchical Clustering
|
Michał Znaleźniak
,
Przemysław Rola
,
Patryk Kaszuba
,
Jacek Tabor
,
Marek Śmieja
|
ECML 2023 |
2023 |
paper |
Interpretowalne metody zrównoważonej sztucznej inteligencji tłumaczące decyzje w sposób intuicyjny
|
Bartosz Zieliński
|
OPUS 24 |
2023 |
grant |
Active Visual Exploration Based on Attention-Map Entropy
|
Adam Pardyl
,
Grzegorz Rypeść
,
Grzegorz Kurzejamski
,
Bartosz Zieliński
,
Tomasz Trzciński
|
IJCAI 2023 |
2023 |
paper |
Sieci prototypowe jako krok do interpretowalnej analizy funkcji białek
|
Witold Wydmański
|
Perły Nauki 2023 |
2023 |
grant |
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models
|
Mohammadreza Banaei
,
Klaudia Bałazy
,
Artur Kasymov
,
Remi Lebret
,
Jacek Tabor
,
Karl Aberer
|
EACL 2023 |
2023 |
paper |
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks
|
Piotr Gaiński
,
Klaudia Bałazy
|
EACL 2023 |
2023 |
paper |
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property Prediction
|
Dawid Rymarczyk
,
Daniel Dobrowolski
,
Tomasz Danel
|
SDM 2023 |
2023 |
paper |
Conditional deep generative models
|
Marek Śmieja
|
OPUS 23 |
2022 |
grant |
Balancing priors in Bayesian Neural Networks
|
Tomasz Kuśmierczyk
|
POLONEZ BIS 2 |
2022 |
grant |
Improving interpretability in deep neural networks
|
Dawid Rymarczyk
|
PRELUDIUM 21 |
2022 |
grant |
Better adaptation in Meta-Learning
|
Marcin Sendera
|
PRELUDIUM 21 |
2022 |
grant |
COeXISTENCE between humans and machines in urban mobility
|
Rafał Kucharski
|
ERC Starting Grant |
2022 |
grant |
Sparsifying Transformer Models with Trainable Representation Pooling
|
Michał Pietruszka
,
Łukasz Borchmann
,
Łukasz Garncarek
|
ACL 2022 (Spotlight) |
2022 |
paper |
SONGs: Self-Organizing Neural Graphs
|
Łukasz Struski
,
Tomasz Danel
,
Marek Śmieja
,
Jacek Tabor
,
Bartosz Zieliński
|
WACV 2023 |
2023 |
paper |
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
|
Mikołaj Sacha
,
Dawid Rymarczyk
,
Łukasz Struski
,
Jacek Tabor
,
Bartosz Zieliński
|
WACV 2023 |
2023 |
paper |
HyperShot: Few-Shot Learning by Kernel HyperNetworks
|
Marcin Sendera
,
Marcin Przewięźlikowski
,
Konrad Karanowski
,
Maciej Zięba
,
Jacek Tabor
,
Przemysław Spurek
|
WACV 2023 |
2023 |
paper |
Discovering wiring patterns influencing neural network performance
|
Aleksandra Nowak
,
Romuald Janik
|
ECML PKDD 2022 |
2022 |
paper |
On the relationship between disentanglement and multi-task learning
|
Łukasz Maziarka
,
Aleksandra Nowak
,
Maciej Wołczyk
,
Andrzej Bedychaj
|
ECML PKDD 2022 |
2022 |
paper |
Multimodal and reinforcement learning platform for personalizing cancer management
|
Krzysztof Geras
|
ARTIQ - AI Centers of Excellence |
2022 |
grant |
Disentangling Transfer in Continual Reinforcement Learning
|
Maciej Wołczyk
,
Michał Zając
,
Razvan Pascanu
,
Łukasz Kuciński
,
Piotr Miłoś
|
NeurIPS 2022 |
2022 |
paper |
FlowHMM: Flow-based continuous hidden Markov models
|
Paweł Lorek
,
Rafał Nowak
,
Tomasz Trzciński
,
Maciej Zięba
|
NeurIPS 2022 |
2022 |
paper |
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
|
Kamil Deja
,
Anna Kuzina
,
Tomasz Trzciński
,
Jakub M. Tomczak
|
NeurIPS 2022 |
2022 |
paper |
Interpretable Image Classification with Differentiable Prototypes Assignment
|
Dawid Rymarczyk
,
Łukasz Struski
,
Michał Górszczak
,
Koryna Lewandowska
,
Jacek Tabor
,
Bartosz Zieliński
|
ECCV |
2022 |
paper |
ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
|
Dawid Rymarczyk
,
Adam Pardyl
,
Jarosław Kraus
,
Aneta Kaczyńska
,
Marek Skomorowski
,
Bartosz Zieliński
|
ECML PKDD |
2022 |
paper |
LIDL: Local Intrinsic Dimension estimation using approximate Likelihood
|
Piotr Tempczyk
,
Rafał Michaluk
,
Łukasz Garncarek
,
Przemysław Spurek
,
Jacek Tabor
,
Adam Golinski
|
ICML 2022 (long presentation) |
2022 |
paper |
Continual Learning with Guarantees via Weight Interval Constraints
|
Maciej Wołczyk
,
Karol Piczak
,
Bartosz Wójcik
,
Łukasz Pustelnik
,
Paweł Morawiecki
,
Jacek Tabor
,
Tomasz Trzciński
,
Przemysław Spurek
|
ICML |
2022 |
paper |
Hypernetworks methods in Meta-Learning
|
Przemysław Spurek
|
Opus 22 |
2022 |
grant |
BabyNet: Residual Transformer Module for Birth Weight Prediction on Fetal Ultrasound Video
|
Szymon Płotka
,
Michał K. Grzeszczyk
,
Robert Brawura-Biskupski-Samaha
,
Paweł Gutaj
,
Michał Lipa
,
Tomasz Trzciński
,
Arkadiusz Sitek
|
MICCAI |
2022 |
paper |
CoNeRF: Controllable Neural Radiance Fields. Computer Vision and Pattern Recognition
|
Kacper Kania
,
Kwang Moo Yi
,
Marek Kowalski
,
Tomasz Trzciński
,
Andrea Tagliasacchi
|
CVPR |
2022 |
paper |
Multiband VAE: Latent Space Partitioning for Knowledge Consolidation in Continual Learning
|
Kamil Deja
,
Paweł Wawrzyński
,
Daniel Marczak
,
Wojciech Masarczyk
,
Tomasz Trzciński
|
IJCAI |
2022 |
paper |
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
|
Maciej Wołczyk
,
Magdalena Proszewska
,
Łukasz Maziarka
,
Maciej Zięba
,
Patryk Wielopolski
,
Rafał Kurczab
,
Marek Śmieja
|
AAAI |
2022 |
paper |
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
|
Maciej Wołczyk
,
Bartosz Wójcik
,
Klaudia Bałazy
,
Igor Podolak
,
Jacek Tabor
,
Marek Śmieja
,
Tomasz Trzciński
|
NeurIPS 2021 |
2021 |
paper |
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
|
Maciej Wołczyk
,
Michał Zając
,
Razvan Pascanu
,
Łukasz Kuciński
,
Piotr Miłoś
|
NeurIPS 2021 |
2021 |
paper |
Non-Gaussian Gaussian Processes for Few-Shot Regression
|
Marcin Sendera
,
Jacek Tabor
,
Aleksandra Nowak
,
Andrzej Bedychaj
,
Massimiliano Patacchiola
,
Tomasz Trzciński
,
Przemysław Spurek
,
Maciej Zięba
|
NeurIPS 2021 |
2021 |
paper |
DUE: End-to-End Document Understanding Benchmark
|
Łukasz Borchmann
,
Michał Pietruszka
,
Tomasz Stanisławek
,
Dawid Jurkiewicz
,
Michał Turski
,
Karolina Szyndler
,
Filip Graliński
|
NeurIPS 2021 |
2021 |
paper |
MEiN scholarship for outstanding young scientists 2021
|
Tomasz Trzciński
|
|
2021 |
scholarship |
MEiN scholarship for outstanding young scientists 2021
|
Marek Śmieja
|
|
2021 |
scholarship |
START 2021
|
Stanisław Jastrzębski
|
|
2021 |
scholarship |
ProtoPShare: Prototypical Parts Sharing for Similarity Discovery in Interpretable Image Classification
|
Dawid Rymarczyk
,
Łukasz Struski
,
Jacek Tabor
,
Bartosz Zieliński
|
SIGKDD |
2021 |
paper |
Robust Learning-Augmented Caching: An Experimental Study
|
Jakub Chłędowski
,
Adam Polak
,
Bartosz Szabucki
,
Konrad Żołna
|
ICML |
2021 |
paper |
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
|
Stanisław Jastrzębski
,
Devansh Arpit
,
Oliver Astrand
,
Giancarlo Kerg
,
Huan Wang
,
Caiming Xiong
,
Richard Socher
,
Kyunghyun Cho*
,
Krzysztof Geras*
|
ICML |
2021 |
paper |
Explaining Self-Supervised Image Representations with Visual Probing
|
Dominika Basaj
,
Witold Oleszkiewicz
,
Igor Sieradzki
,
Michał Górszczak
,
Barbara Rychalska
,
Tomasz Trzciński
,
Bartosz Zieliński
|
IJCAI |
2021 |
paper |
Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning
|
Dawid Rymarczyk
,
Adriana Borowa
,
Jacek Tabor
,
Bartosz Zieliński
|
WACV |
2021 |
paper |
Combination of Molecular Simulation and Deep Learning for De Novo Drug Design
|
Tomasz Danel
|
Preludium 19 |
2020 |
grant |
Cramer-Wold AutoEncoder
|
Szymon Knop
,
Jacek Tabor
,
Przemysław Spurek
,
Igor Podolak
,
Marcin Mazur
,
Stanisław Jastrzębski
|
JMLR |
2020 |
paper |
Finding the Optimal Network Depth in Classification Tasks
|
Bartosz Wójcik
,
Maciej Wołczyk
,
Klaudia Bałazy
,
Jacek Tabor
|
ECML PKDD |
2020 |
paper |
Hypernetwork approach to generating point clouds
|
Przemysław Spurek
,
Sebastian Winczowski
,
Jacek Tabor
,
Maciej Zamorski
,
Maciej Zięba
,
Tomasz Trzciński
|
ICML |
2020 |
paper |
Transformer-based methods for novel active chemical compounds
|
Łukasz Maziarka
|
Preludium |
2020 |
grant |
Evolutionary-Neural Hybrid Agents for Architecture Search
|
Krzysztof Maziarz
,
Mingxing Tan
,
Andrey Khorlin
,
Marin Georgiev
,
Andrea Gesmundo
|
ICML Workshop on AutoML 2019; 1st place in Data Science Masters - best Master's thesis competition, Applied track |
2019 |
paper |
Bio-inspired artificial neural networks
|
Jacek Tabor
|
FNP TEAM-NET |
2019-2023 |
grant |
The Break-Even Point on the Optimization Trajectories of Deep Neural Networks
|
Stanisław Jastrzębski
,
Maciej Szymczak
,
Stanislav Fort
,
Devansh Arpit
,
Jacek Tabor
,
Kyunghyun Cho*
,
Krzysztof Geras*
|
ICLR 2020 (Spotlight) |
2020 |
paper |
Persistence bag of words for topological data analysis
|
Bartosz Zieliński
,
Michał Lipiński
,
Mateusz Juda
,
Matthias Zeppelzauer
,
Paweł Dłotko
|
IJCAI |
2019 |
paper |
Parameter-Efficient Transfer Learning for NLP
|
Neil Houlsby
,
Andrei Giurgiu
,
Stanisław Jastrzębski
,
Bruna Morrone
,
Quentin de Laroussilhe
,
Andrea Gesmundo
,
Mona Attariyan
,
Sylvain Gelly
|
ICML |
2019 |
paper |
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length
|
Stanisław Jastrzębski
,
Zachary Kenton
,
Nicolas Ballas
,
Asja Fischer
,
Yoshua Bengio
,
Amos Storkey
|
ICLR |
2019 |
paper |
Distribution-Interpolation Trade off in Generative Models
|
Damian Leśniak
,
Igor Sieradzki
,
Igor Podolak
|
ICLR |
2019 |
paper |
Processing of missing data by neural networks
|
Marek Śmieja
,
Łukasz Struski
,
Jacek Tabor
,
Bartosz Zieliński
,
Przemysław Spurek
|
NeurIPS 2018 |
2018 |
paper |
Efficient unsupervised learning with applications in deep learning
|
prof. Jacek Tabor
,
dr Marek Śmieja
,
dr Przemysław Spurek
,
dr Łukasz Struski
|
NCN OPUS 13 |
2018-2021 |
grant |
Deep Learning in representation of long sequential data
|
mgr Konrad Żołna
|
NCN ETIUDA 6 |
2018-2019 |
prize |
Additional information in data clustering and related areas
|
dr Marek Śmieja
,
dr Przemysław Spurek
,
dr Łukasz Struski
|
2017-2020 NCN SONATA 11 |
2017-2020 |
grant |
New representation learning methods in Deep Learning
|
mgr Stanisław Jastrzębski
|
NCN ETIUDA 5 |
2017-2019 |
prize |
Theory of missing data
|
prof. Jacek Tabor
,
dr Łukasz Struski
,
dr Marek Śmieja
,
dr Bartosz Zieliński
|
NCN OPUS 10 |
2016-2019 |
grant |
Clustering algorithm, which uses generalized Gaussian distribution and non-normal distributions
|
dr Przemysław Spurek
|
NCN SONATA 10 |
2016-2019 |
grant |
Detectors and descriptors of the key points based on the topological information
|
dr Bartosz Zieliński
,
dr Mateusz Juda
|
NCN SONATA 10 |
2016-2019 |
grant |
Algorithmical aspects of synchronization
|
dr Adam Roman
|
NCN OPUS 9 |
2016-2019 |
grant |
Best paper award at CORES'15
|
mgr Wojciech Czarnecki
,
dr hab. Jacek Tabor
|
|
2015 |
prize |
Application of neural networks in politology
|
dr hab. Igor Podolak (grant coordinated by dr hab. Łukasz Wordliczek)
|
NCN OPUS 8 |
2015-2018 |
grant |
Knowledge enriched sparse word embedding
|
lic. Stanisław Jastrzębski (under supervision of dr hab. Jacek Tabor)
|
MNiSW Diamentowy Grant 4 |
2015-2018 |
grant |
Minimal Memory Clustering Paradigm
|
dr hab. Jacek Tabor
,
dr hab. Igor Podolak
,
prof. dr hab. Andrzej Bojarski
,
mgr Przemysław Spurek
,
mgr Marek Śmieja
,
mgr Wojciech Czarnecki
,
mgr Sabina Smusz
|
NCN OPUS 7 |
2015-2017 |
grant |
Development of machine learning methods with applications to chemical compound activity prediction
|
mgr Marek Śmieja
|
NCN PRELUDIUM 7 |
2015-2017 |
grant |
Rector prize for scientific achievements
|
prof. Jacek Tabor
,
dr Wojciech Czarnecki
,
dr Przemysław Spurek
|
UJ |
2015 |
prize |
Scholarship of Polish Minister Of Science
|
lic. Stanisław Jastrzębski
|
MNiSW |
2015 |
prize |
A novel approach to de novo genome assembly problem based on UCT
|
mgr Ewa Matczyńska
|
NCN PRELUDIUM 6 |
2014-2017 |
grant |
The memory center
|
mgr Przemysław Spurek
|
NCN PRELUDIUM 5 |
2014-2017 |
grant |
Novel active learning querying strategy for the machine learning models
|
mgr Wojciech M. Czarnecki
|
NCN PRELUDIUM 5 |
2014-2017 |
grant |
4th place in TRADESHIFT competition
|
lic. Stanisław Jastrzębski
,
Rafał Józefowicz (Google)
,
mgr Wojciech Czarnecki
|
|
2014 |
prize |
5th place in CONNECTOMICS competition
|
mgr Wojciech Czarnecki
,
Rafał Józefowicz (Google)
|
|
2014 |
prize |
Entropy of the mixture of sources
|
dr hab Jacek Tabor
,
mgr Marek Śmieja
|
|
2013-2015, NCN OPUS 1 |
grant |
Second prize at the SMP competition
|
Stanisław Jastrzębski (under supervision of dr hab. Igor Podolak)
|
|
2013 |
prize |
Best presentation at CISIM'13
|
mgr Przemysław Spurek
|
|
2013 |
prize |