Portrait de Yorguin Mantilla Ramos

Yorguin Mantilla Ramos

yorguin.mantilla@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Karim Jerbi
Sujets de recherche
Apprentissage automatique scientifique
IA inspirée du cerveau
IA pour la science
Interprétabilité
Interprétabilité mécanistique
NeuroIA
Neurosciences
Neurosciences computationnelles
Science cognitive
Site web
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 … (voir 1 de plus)
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… (voir plus)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 (prépublication)
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 … (voir 1 de plus)
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… (voir plus)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 (prépublication)
doi.org
arxiv.org