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pro vyhledávání: '"Kucinski A"'
Denoising Diffusion Probabilistic Models (DDPMs) achieve state-of-the-art performance in synthesizing new images from random noise, but they lack meaningful latent space that encodes data into features. Recent DDPM-based editing techniques try to mit
Externí odkaz:
http://arxiv.org/abs/2410.23530
Autor:
Powalski, Rafał, Klockiewicz, Bazyli, Jaśkowski, Maciej, Topolski, Bartosz, Dąbrowski-Tumański, Paweł, Wiśniewski, Maciej, Kuciński, Łukasz, Miłoś, Piotr, Plewczynski, Dariusz
Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential
Externí odkaz:
http://arxiv.org/abs/2411.00004
Autor:
Bortkiewicz, Michał, Pałucki, Władek, Myers, Vivek, Dziarmaga, Tadeusz, Arczewski, Tomasz, Kuciński, Łukasz, Eysenbach, Benjamin
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 learning in other domains aims to find patterns in a fixed dataset, se
Externí odkaz:
http://arxiv.org/abs/2408.11052
We introduce RoboMorph, an automated approach for generating and optimizing modular robot designs using large language models (LLMs) and evolutionary algorithms. In this framework, we represent each robot design as a grammar and leverage the capabili
Externí odkaz:
http://arxiv.org/abs/2407.08626
Autor:
Zawalski, Michał, Góral, Gracjan, Tyrolski, Michał, Wiśnios, Emilia, Budrowski, Franciszek, Kuciński, Łukasz, Miłoś, Piotr
Efficiently tackling combinatorial reasoning problems, particularly the notorious NP-hard tasks, remains a significant challenge for AI research. Recent efforts have sought to enhance planning by incorporating hierarchical high-level search strategie
Externí odkaz:
http://arxiv.org/abs/2406.03361
Autor:
Kuciński, Łukasz, Drzewakowski, Witold, Olko, Mateusz, Kozakowski, Piotr, Maziarka, Łukasz, Nowakowska, Marta Emilia, Kaiser, Łukasz, Miłoś, Piotr
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 transformer models with time series-specific architectural biases. In th
Externí odkaz:
http://arxiv.org/abs/2403.05713
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten. Existing generative strategies combat catastrophic forgetting by randomly sampling rehearsal examples from a generat
Externí odkaz:
http://arxiv.org/abs/2403.03938
Autor:
Wołczyk, Maciej, Cupiał, Bartłomiej, Ostaszewski, Mateusz, Bortkiewicz, Michał, Zając, Michał, Pascanu, Razvan, Kuciński, Łukasz, Miłoś, Piotr
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models. However, fine-tuning reinforcement learning (RL) models remains a challen
Externí odkaz:
http://arxiv.org/abs/2402.02868
Autor:
Staniszewski, Konrad, Tworkowski, Szymon, Jaszczur, Sebastian, Zhao, Yu, Michalewski, Henryk, Kuciński, Łukasz, Miłoś, Piotr
Recent advancements in long-context large language models have attracted significant attention, yet their practical applications often suffer from suboptimal context utilization. This study investigates structuring training data to enhance semantic i
Externí odkaz:
http://arxiv.org/abs/2312.17296
Autor:
Mikuła, Maciej, Tworkowski, Szymon, Antoniak, Szymon, Piotrowski, Bartosz, Jiang, Albert Qiaochu, Zhou, Jin Peng, Szegedy, Christian, Kuciński, Łukasz, Miłoś, Piotr, Wu, Yuhuai
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 knowledge and engineering effort are applied to this task. In contrast, th
Externí odkaz:
http://arxiv.org/abs/2303.04488