Publications

Latent Representation Learning for Multimodal Brain Activity Translation
Arman Afrasiyabi
Dhananjay Bhaskar
Erica Lindsey Busch
Laurent Caplette
Rahul Singh
Guillaume Lajoie
Nicholas B Turk-Browne
Smita Krishnaswamy
Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordi… (see more)ngs such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data sources remains a challenge, which limits a comprehensive understanding of brain function. We present the Spatiotemporal Alignment of Multimodal Brain Activity (SAMBA) framework, which bridges the spatial and temporal resolution gaps across modalities by learning a unified latent space free of modality-specific biases. SAMBA introduces a novel attention-based wavelet decomposition for spectral filtering of electrophysiological recordings, graph attention networks to model functional connectivity between functional brain units, and recurrent layers to capture temporal autocorrelations in brain signal. We show that the training of SAMBA, aside from achieving translation, also learns a rich representation of brain information processing. We showcase this classify external stimuli driving brain activity from the representation learned in hidden layers of SAMBA, paving the way for broad downstream applications in neuroscience research and clinical contexts.
2025-04-06
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
arxiv.org
LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Eleonora Mancini
Francesco Paissan
Mirco Ravanelli
Cem Subakan
2025-04-06
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)
doi.org
arxiv.org
Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition
Muhammad Osama Zeeshan
Marco Pedersoli
Alessandro Lameiras Koerich
Eric Grange
2025-04-05
ArXiv (preprint)
arxiv.org
arxiv.org
Accelerated learning of a noninvasive human brain-computer interface via manifold geometry
Erica Lindsey Busch
E. Chandra Fincke
Guillaume Lajoie
Smita Krishnaswamy
Nicholas B Turk-Browne
2025-04-03
bioRxiv (preprint)
doi.org
Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing
Seif Mzoughi
Mohamed Elshafeia
Foutse Khomh
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
Spinal Cord Tract Integrity in Degenerative Cervical Myelopathy.
Newton Cho
Abdul Al-Shawwa
W. Bradley Jacobs
Nathan Evaniew
Jacques Bouchard
Steven Casha
Stephan duPlessis
Peter Lewkonia
Fred Nicholls
Alex Soroceanu
Ganesh Swamy
Kenneth C. Thomas
Michael M.H. Yang
Julien Cohen-Adad
David W. Cadotte
2025-04-03
Neurosurgery (published)
doi.org
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi
Ahmed Haj Yahmed
Mohamed Elshafei
Foutse Khomh
Diego Elias Costa
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
Why do LLMs attend to the first token?
Federico Barbero
'Alvaro Arroyo
Xiangming Gu
Christos Perivolaropoulos
Michael M. Bronstein
Petar Velivckovi 'c
Razvan Pascanu
2025-04-03
ArXiv (preprint)
arxiv.org
arxiv.org
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanovi'c
Arkil Patel
Vaibhav Adlakha
Milad Aghajohari
Parishad BehnamGhader
Mehar Bhatia
Aditi Khandelwal
Austin Kraft
Benno Krojer
Xing Han Lu
Xing Han Lu
Nicholas Meade
Dongchan Shin
Amirhossein Kazemnejad
Gaurav Kamath
Marius Mosbach
Karolina Stanczak
Siva Reddy
2025-04-02
ArXiv (preprint)
arxiv.org
arxiv.org
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
Sara Vera Marjanovi'c
Arkil Patel
Vaibhav Adlakha
Milad Aghajohari
Parishad BehnamGhader
Mehar Bhatia
Aditi Khandelwal
Austin Kraft
Benno Krojer
Xing Han Lu
Xing Han Lu
Nicholas Meade
Dongchan Shin
Amirhossein Kazemnejad
Gaurav Kamath
Marius Mosbach
Karolina Stanczak
Siva Reddy
Large Reasoning Models like DeepSeek-R1 mark a fundamental shift in how LLMs approach complex problems. Instead of directly producing an ans… (see more)wer for a given input, DeepSeek-R1 creates detailed multi-step reasoning chains, seemingly"thinking"about a problem before providing an answer. This reasoning process is publicly available to the user, creating endless opportunities for studying the reasoning behaviour of the model and opening up the field of Thoughtology. Starting from a taxonomy of DeepSeek-R1's basic building blocks of reasoning, our analyses on DeepSeek-R1 investigate the impact and controllability of thought length, management of long or confusing contexts, cultural and safety concerns, and the status of DeepSeek-R1 vis-\`a-vis cognitive phenomena, such as human-like language processing and world modelling. Our findings paint a nuanced picture. Notably, we show DeepSeek-R1 has a 'sweet spot' of reasoning, where extra inference time can impair model performance. Furthermore, we find a tendency for DeepSeek-R1 to persistently ruminate on previously explored problem formulations, obstructing further exploration. We also note strong safety vulnerabilities of DeepSeek-R1 compared to its non-reasoning counterpart, which can also compromise safety-aligned LLMs.
2025-04-02
ArXiv (preprint)
arxiv.org
arxiv.org
A Truncated Newton Method for Optimal Transport
Mete Kemertas
Amir-massoud Farahmand
Allan D. Jepson
2025-04-02
ArXiv (preprint)
arxiv.org
arxiv.org
A Truncated Newton Method for Optimal Transport
Mete Kemertas
Amir-massoud Farahmand
Allan D. Jepson
2025-04-02
ArXiv (preprint)
doi.org
arxiv.org