Portrait of Yorguin Mantilla Ramos

Yorguin Mantilla Ramos

yorguin.mantilla@mila.quebec
Master's Research - Université de Montréal
Supervisor
Karim Jerbi
Research Topics
AI for Science
Brain-inspired AI
Cognitive Science
Computational Neuroscience
Interpretability
Mechanistic Interpretability
NeuroAI
Neuroscience
Scientific Machine Learning
Website
Google Scholar
GitHub

Publications

The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100
Francesco Mantegna
Gereon Elvers
Dulhan Jayalath
Gilad Landau
Tasha Kim
Miran Ozdogan
Luisa Kurth
Teyun Kwon
SungJun Cho
Benjamin Ballyk
Alex Fung
Anna Greer
Pratik Somaiya
Christian Herff
Yorguin Mantilla Ramos
Hamza Abdelhedi
Karim Jerbi
Greg Farquhar
Brendan Shillingford
Mark Woolrich … (see 1 more)
Oiwi Parker Jones
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Desi… (see more)gned to progress from foundational tasks toward the linguistic complexity required for a practical brain-computer interface (BCI), it set the stage with speech detection and phoneme classification tasks. Winning submissions reached F1-macro scores of 95.6% and 73.6% on the respective tasks (Elvers et al., 2026), highly significant advances. This success was built on the LibriBrain dataset (Özdogan et al., 2025), the largest within-subject MEG dataset recorded at the time with
2026-09-02
arXiv (preprint)
doi.org
arxiv.org
The 2026 PNPL Competition: Word Classification and Efficient Cross-Subject Generalisation in LibriBrain100
Francesco Mantegna
Gereon Elvers
Dulhan Jayalath
Gilad Landau
Tasha Kim
Miran Ozdogan
Luisa Kurth
Teyun Kwon
SungJun Cho
Benjamin Ballyk
Alex Fung
Anna Greer
Pratik Somaiya
Christian Herff
Yorguin Mantilla Ramos
Hamza Abdelhedi
Karim Jerbi
Greg Farquhar
Brendan Shillingford
Mark Woolrich … (see 1 more)
Oiwi Parker Jones
The ambition of the 2025 PNPL competition (Landau et al., 2025) was to launch a multi-year curriculum for non-invasive speech decoding. Desi… (see more)gned to progress from foundational tasks toward the linguistic complexity required for a practical brain-computer interface (BCI), it set the stage with speech detection and phoneme classification tasks. Winning submissions reached F1-macro scores of 95.6% and 73.6% on the respective tasks (Elvers et al., 2026), highly significant advances. This success was built on the LibriBrain dataset (Özdogan et al., 2025), the largest within-subject MEG dataset recorded at the time with
2026-09-02
arXiv (preprint)
doi.org
arxiv.org