no code implementations • 27 Feb 2019 • Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, Marek Śmieja
Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network.
no code implementations • 21 Jun 2019 • Marek Śmieja, Maciej Wołczyk, Jacek Tabor, Bernhard C. Geiger
We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Maciej Wołczyk, Jacek Tabor, Marek Śmieja, Szymon Maszke
We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions.
1 code implementation • 17 Apr 2020 • Bartosz Wójcik, Maciej Wołczyk, Klaudia Bałazy, Jacek Tabor
We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads.
1 code implementation • NeurIPS 2021 • Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
Continual learning (CL) -- the ability to continuously learn, building on previously acquired knowledge -- is a natural requirement for long-lived autonomous reinforcement learning (RL) agents.
1 code implementation • NeurIPS 2021 • Maciej Wołczyk, Bartosz Wójcik, Klaudia Bałazy, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications.
1 code implementation • 18 Sep 2021 • Maciej Wołczyk, Magdalena Proszewska, Łukasz Maziarka, Maciej Zięba, Patryk Wielopolski, Rafał Kurczab, Marek Śmieja
Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling.
no code implementations • 27 Sep 2021 • Oliver Scheel, Luca Bergamini, Maciej Wołczyk, Błażej Osiński, Peter Ondruska
In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations.
no code implementations • 28 Sep 2021 • Matt Vitelli, Yan Chang, Yawei Ye, Maciej Wołczyk, Błażej Osiński, Moritz Niendorf, Hugo Grimmett, Qiangui Huang, Ashesh Jain, Peter Ondruska
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
no code implementations • 7 Oct 2021 • Łukasz Maziarka, Aleksandra Nowak, Maciej Wołczyk, Andrzej Bedychaj
One of the main arguments behind studying disentangled representations is the assumption that they can be easily reused in different tasks.
1 code implementation • 16 Jun 2022 • Maciej Wołczyk, Karol J. Piczak, Bartosz Wójcik, Łukasz Pustelnik, Paweł Morawiecki, Jacek Tabor, Tomasz Trzciński, Przemysław Spurek
We introduce a new training paradigm that enforces interval constraints on neural network parameter space to control forgetting.
no code implementations • 28 Jun 2022 • Paweł Morawiecki, Andrii Krutsylo, Maciej Wołczyk, Marek Śmieja
Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks.
no code implementations • 28 Sep 2022 • Maciej Wołczyk, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
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, limiting the applicability of continual learning solutions to realistic scenarios.
2 code implementations • 29 Nov 2022 • Samuel Kessler, Mateusz Ostaszewski, Michał Bortkiewicz, Mateusz Żarski, Maciej Wołczyk, Jack Parker-Holder, Stephen J. Roberts, Piotr Miłoś
World models power some of the most efficient reinforcement learning algorithms.
1 code implementation • 22 Dec 2023 • Simon Schug, Seijin Kobayashi, Yassir Akram, Maciej Wołczyk, Alexandra Proca, Johannes von Oswald, Razvan Pascanu, João Sacramento, Angelika Steger
This allows us to relate the problem of compositional generalization to that of identification of the underlying modules.
no code implementations • 5 Feb 2024 • Maciej Wołczyk, Bartłomiej Cupiał, Mateusz Ostaszewski, Michał Bortkiewicz, Michał Zając, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models.
1 code implementation • 4 Apr 2024 • Adam Pardyl, Michał Wronka, Maciej Wołczyk, Kamil Adamczewski, Tomasz Trzciński, Bartosz Zieliński
Active Visual Exploration (AVE) is a task that involves dynamically selecting observations (glimpses), which is critical to facilitate comprehension and navigation within an environment.