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Agnieszka Slowik

Alumni

Publications

Sex-specific lesion pattern of functional outcomes after stroke
Anna K. Bonkhoff
Martin Bretzner
Sungmin Hong
Markus D. Schirmer
Alexander Cohen
Robert W. Regenhardt
Kathleen L. Donahue
Marco J. Nardin
Adrian V. Dalca
Anne-Katrin Giese
Mark R. Etherton
Brandon L. Hancock
Steven J. T. Mocking
Elissa C. McIntosh
John Attia
Oscar R. Benavente
Stephen Bevan
John W. Cole
Amanda Donatti
Christoph J. Griessenauer … (see 39 more)
Laura Heitsch
Lukas Holmegaard
Katarina Jood
Jordi Jimenez-Conde
Steven J. Kittner
Robin Lemmens
Christopher R. Levi
Caitrin W. McDonough
James F. Meschia
Chia-Ling Phuah
Arndt Rolfs
Stefan Ropele
Jonathan Rosand
Jaume Roquer
Tatjana Rundek
Ralph L. Sacco
Reinhold Schmidt
Pankaj Sharma
Agnieszka Slowik
Martin Söderholm
Alessandro Sousa
Tara M. Stanne
Daniel Strbian
Turgut Tatlisumak
Vincent Thijs
Achala Vagal
Johan Wasselius
Daniel Woo
Ramin Zand
Patrick F. McArdle
Bradford B. Worrall
Christina Jern
Arne G. Lindgren
Jane Maguire
Michael D. Fox
Danilo Bzdok
Ona Wu
Natalia S. Rost
Anna K. Martin Sungmin Markus D. Alexander Robert W. Kathleen L. Marco J. Adrian V. Anne-Katrin Mark R. Brandon L. Steven J. T. Elissa C. John Oscar R. Stephen John W. Amanda Christoph J. Laura Lukas Katarina Jordi Steven J. Robin Christopher R. Caitrin W. James F. Chia-Ling Arndt Stefan Jonathan Jaume Tatjana Ralph L. Reinhold Pankaj Agnieszka Martin Alessandro Tara M. Daniel Turgut Vincent Achala Johan Daniel Ramin Patrick F. Bradford B. Christina Arne G. Jane Michael D. Danilo Ona Natalia S. Bonkhoff
Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevan… (see more)t sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.
2022-01-30
Brain Communications (published)
doi.org
Neural Function Modules with Sparse Arguments: A Dynamic Approach to Integrating Information across Layers
Alex Lamb
Anirudh Goyal
Agnieszka Słowik
Michael Mozer
Philippe Beaudoin
Yoshua Bengio
Feed-forward neural networks consist of a sequence of layers, in which each layer performs some processing on the information from the previ… (see more)ous layer. A downside to this approach is that each layer (or module, as multiple modules can operate in parallel) is tasked with processing the entire hidden state, rather than a particular part of the state which is most relevant for that module. Methods which only operate on a small number of input variables are an essential part of most programming languages, and they allow for improved modularity and code re-usability. Our proposed method, Neural Function Modules (NFM), aims to introduce the same structural capability into deep learning. Most of the work in the context of feed-forward networks combining top-down and bottom-up feedback is limited to classification problems. The key contribution of our work is to combine attention, sparsity, top-down and bottom-up feedback, in a flexible algorithm which, as we show, improves the results in standard classification, out-of-domain generalization, generative modeling, and learning representations in the context of reinforcement learning.
2021-03-17
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press
Structural Inductive Biases in Emergent Communication
Agnieszka Słowik
Abhinav Gupta
William L. Hamilton
Mateja Jamnik
Sean B. Holden
Christopher Pal
In order to communicate, humans flatten a complex representation of ideas and their attributes into a single word or a sentence. We investig… (see more)ate the impact of representation learning in artificial agents by developing graph referential games. We empirically show that agents parametrized by graph neural networks develop a more compositional language compared to bag-of-words and sequence models, which allows them to systematically generalize to new combinations of familiar features.
2020-02-03
arXiv.org (preprint)
doi.org
arxiv.org