8 citations · 17 across the 9 of their papers we have counts for
9 papers
RapidDock: Unlocking Proteome-scale Molecular Docking
Rafał Powalski, Bazyli Klockiewicz, Maciej Jaśkowski +6
Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortuna…
Accelerating Goal-Conditioned RL Algorithms and Research
Michał Bortkiewicz, Władysław Pałucki, Vivek Myers +4
Self-supervision has the potential to transform reinforcement learning (RL), paralleling the breakthroughs it has enabled in other areas of machine learning. While self-supervised…
What Matters in Hierarchical Search for Combinatorial Reasoning Problems?
Michał Zawalski, Gracjan Góral, Michał Tyrolski +5
Efficiently tackling combinatorial reasoning problems, particularly the notorious NP-hard tasks, remains a significant challenge for AI research. Recent efforts have sought to enha…
tsGT: Stochastic Time Series Modeling With Transformer
Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko +5
Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data. Recently, there has been a surge of deterministic tr…
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski +5
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. How…
Structured Packing in LLM Training Improves Long Context Utilization
Konrad Staniszewski, Szymon Tworkowski, Sebastian Jaszczur +4
Recent advancements in long-context large language models have attracted significant attention, yet their practical applications often suffer from suboptimal context utilization. T…
Magnushammer: A Transformer-Based Approach to Premise Selection
Maciej Mikuła, Szymon Tworkowski, Szymon Antoniak +7
This paper presents a novel approach to premise selection, a crucial reasoning task in automated theorem proving. Traditionally, symbolic methods that rely on extensive domain know…
Disentangling Transfer in Continual Reinforcement Learning
Maciej Wołczyk, Michał Zając, Razvan Pascanu +2
The ability of continual learning systems to transfer knowledge from previously seen tasks in order to maximize performance on new tasks is a significant challenge for the field, l…
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Michał Zawalski, Michał Tyrolski, Konrad Czechowski +6
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgo…