1 code implementation • 5 Jun 2022 • Santiago Cuervo, Adrian Łańcucki, Ricard Marxer, Paweł Rychlikowski, Jan Chorowski
The success of deep learning comes from its ability to capture the hierarchical structure of data by learning high-level representations defined in terms of low-level ones.
1 code implementation • 29 Oct 2021 • Santiago Cuervo, Maciej Grabias, Jan Chorowski, Grzegorz Ciesielski, Adrian Łańcucki, Paweł Rychlikowski, Ricard Marxer
We investigate the performance on phoneme categorization and phoneme and word segmentation of several self-supervised learning (SSL) methods based on Contrastive Predictive Coding (CPC).
1 code implementation • 22 Jun 2021 • Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski, Adrian Łańcucki, Ricard Marxer, Mateusz Opala, Piotr Pusz, Paweł Rychlikowski, Michał Stypułkowski
We present a number of low-resource approaches to the tasks of the Zero Resource Speech Challenge 2021.
no code implementations • EACL (BSNLP) 2021 • Paweł Rychlikowski, Bartłomiej Najdecki, Adrian Łańcucki, Adam Kaczmarek
In this paper we describe our submissions to the 2nd and 3rd SlavNER Shared Tasks held at BSNLP 2019 and BSNLP 2021, respectively.
1 code implementation • 24 Apr 2021 • Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski, Adrian Łańcucki, Ricard Marxer, Mateusz Opala, Piotr Pusz, Paweł Rychlikowski, Michał Stypułkowski
We investigate the possibility of forcing a self-supervised model trained using a contrastive predictive loss to extract slowly varying latent representations.
no code implementations • 21 May 2018 • Jan Chorowski, Adrian Łańcucki, Szymon Malik, Maciej Pawlikowski, Paweł Rychlikowski, Paweł Zykowski
We present Poetwannabe, a chatbot submitted by the University of Wroc{\l}aw to the NIPS 2017 Conversational Intelligence Challenge, in which it ranked first ex-aequo.
2 code implementations • 29 May 2017 • Michał Zapotoczny, Paweł Rychlikowski, Jan Chorowski
We analyze the representations of characters and words that are learned by the network to establish which properties of languages were accounted for.
1 code implementation • 12 Sep 2016 • Jan Chorowski, Michał Zapotoczny, Paweł Rychlikowski
We present a dependency parser implemented as a single deep neural network that reads orthographic representations of words and directly generates dependencies and their labels.