Publications

LMAC-TD: Producing Time Domain Explanations for Audio Classifiers
Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition
Muhammad Osama Zeeshan
Alessandro Lameiras Koerich
Eric Grange
Accelerated learning of a noninvasive human brain-computer interface via manifold geometry
Erica Lindsey Busch
E. Chandra Fincke
Nicholas B Turk-Browne
Evaluating and Enhancing Segmentation Model Robustness with Metamorphic Testing
Seif Mzoughi
Mohamed Elshafeia
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
David W. Cadotte
Towards Assessing Deep Learning Test Input Generators
Seif Mzoughi
Ahmed Haj Yahmed
Mohamed Elshafei
Diego Elias Costa
Why do LLMs attend to the first token?
Federico Barbero
'Alvaro Arroyo
Xiangming Gu
Christos Perivolaropoulos
Michael M. Bronstein
Petar Velivckovi 'c
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning
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.
A Truncated Newton Method for Optimal Transport
Mete Kemertas
Allan D. Jepson
A Truncated Newton Method for Optimal Transport
Mete Kemertas
Allan D. Jepson
Addressing Missing Modality Challenges in MRI Images: A Comprehensive Review
Reza Azad
Mohammad Dehghanmanshadi
Nika Khosravi
Dorit Merhof