Achievements
AAAI | Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference | Bartosz Wójcik, Alessio Devoto, Karol Pustelnik, Pasquale Minervini, Simone Scardapane | 2025 | |
ICLR | Workshop on Sparsity in LLMs (SLLM): Deep Dive into Mixture of Experts, Quantization, Hardware, and Inference | Tianlong Chen, Utku Evci, Yani Ioannou, Berivan Isik, Shiwei Liu, Mohammed Adnan, Aleksandra Nowak, Ashwinee Panda | 2025 | |
Prime Minister's Prize in the category of outstanding doctoral dissertation. | Deep learning methods in pharmaceutical sciences | Tomasz Danel | 2024 | |
WACV | Beyond Grids: Exploring Elastic Input Sampling for Vision Transformers | Adam Pardyl, Grzegorz Kurzejamski, Jan Olszewski, Tomasz Trzciński, Bartosz Zieliński | 2025 | |
Knowledge-Based Systems | Augmentation-aware Self-supervised Learning with Conditioned Projector | Marcin Przewięźlikowski, Mateusz Pyla, Bartosz Zieliński, Bartłomiej Twardowski, Jacek Tabor, Marek Śmieja | 2024 | |
NeurIPS | D-MiSo: Editing Dynamic 3D Scenes using Multi-Gaussians Soup | Joanna Waczyńska, Piotr Borycki, Joanna Kaleta, Sławomir Tadeja, Przemysław Spurek | 2024 | |
NeurIPS | Exploiting Activation Sparsity with Dense to Dynamic-k Mixture-of-Experts Conversion | Filip Szatkowski, Bartosz Wójcik, Mikołaj Piórczyński, Simone Scardapane | 2024 | |
NeurIPS | RGFN: Synthesizable Molecular Generation Using GFlowNets | Michał Koziarski, Andrei Rekesh, Dmytro Shevchuk, Almer van der Sloot, Piotr Gaiński, Yoshua Bengio, Cheng-Hao Liu, Mike Tyers, Robert A. Batey | 2024 | |
NeurIPS | Amortizing intractable inference in diffusion models for vision, language, and control | Siddarth Venkatraman*, Moksh Jain*, Luca Scimeca*, Minsu Kim*, Marcin Sendera*, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, Alexandre Adam, Jarrid Rector-Brooks, Yoshua Bengio, Glen Berseth, Nikolay Malkin | 2024 | |
NeurIPS | Improved off-policy training of diffusion samplers | Marcin Sendera, Minsu Kim, Sarthak Mittal, Pablo Lemos, Luca Scimeca, Jarrid Rector-Brooks, Alexandre Adam, Yoshua Bengio, Nikolay Malkin | 2024 | |
WACV | Token Recycling for Efficient Sequential Inference with Vision Transformers | Jan Olszewski, Dawid Rymarczyk, Piotr Wójcik, Mateusz Pach, Bartosz Zieliński | 2025 | |
WACV | GeoGuide: Geometric guidance of diffusion models | Mateusz Poleski, Jacek Tabor, Przemysław Spurek | 2025 | |
Neurocomputing | HyperMAML: Few-Shot Adaptation of Deep Models with Hypernetworks | Marcin Przewięźlikowski, Przemysław Przybysz, Jacek Tabor, Maciej Zięba, Przemysław Spurek | 2024 | |
ECCV | AdaGlimpse: Active Visual Exploration with Arbitrary Glimpse Position and Scale | Adam Pardyl, Michał Wronka, Maciej Wołczyk, Kamil Adamczewski, Tomasz Trzciński, Bartosz Zieliński | 2024 | |
FIRST TEAM FENG | Interpretowalne i interaktywne wielomodalne wyszukiwanie w procesie odkrywania leków. | Bartosz Zieliński | 2024 | |
FIRST TEAM FENG | Efektywne renderowanie obiektów 3D reprezentowanych za pomocą NeRF w środowisku rozszerzonej rzeczywistości. | Przemyslaw Spurek | 2024 | |
ECML PKDD | A deep cut into Split Federated Self-Supervised Learning | Marcin Przewięźlikowski, Marcin Osial, Marek Śmieja, Bartosz Zieliński | 2024 | |
ICML | Sparser, Better, Deeper, Stronger: Improving Sparse Training with Exact Orthogonal Initialization | Aleksandra Nowak, Łukasz Gniecki, Filip Szatkowski, Jacek Tabor | 2024 | |
ICLR | Prediction Error-based Classification for Class-Incremental Learning | Michał Zając, Tinne Tuytelaars, Gido M. van de Ven | 2024 | |
ICLR | Divide and not forget Ensemble of selectively trained experts in Continual Learning | Grzegorz Rypeść, Sebastian Cygert, Valeriya Khan, Tomasz Trzcinski, Bartosz Zieliński, Bartłomiej Twardowski | 2024 | |
AAMAS | Modelling the Rise and Fall of Two-Sided Mobility Markets with Microsimulation | Farnoud Ghasemi, Rafał Kucharski | 2024 | |
AAAI | Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations | Mikołaj Sacha, Bartosz Jura, Dawid Rymarczyk, Łukasz Struski, Jacek Tabor, Bartosz Zieliński | 2024 | |
OPUS 25 | Meta-learning in Deep Neural Networks | Jacek Tabor | 2023 | |
PRELUDIUM 22 | Where to look next - guiding active visual exploration with internal model uncertainty | Adam Pardyl | 2023 | |
PRELUDIUM 22 | Improving the transferability of self-supervised learning models | Marcin Przewięźlikowski | 2023 |