Antonio Henrique de Oliveira Fonseca — accepted AI-conference papers

2024

BrainLM: A foundation model for brain activity recordings

Josue Ortega Caro, Antonio Henrique de Oliveira Fonseca, Syed A Rizvi, Matteo Rosati, Christopher Averill, James L Cross +5

ICLR 2024poster

We introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for…

Cited by 34 Source ↗PDF ↗Scholar ↗

2024

Cell2Sentence: Teaching Large Language Models the Language of Biology

Daniel Levine, Syed A Rizvi, Sacha Lévy, Nazreen Pallikkavaliyaveetil, David Zhang, Xingyu Chen +12

ICML 2024poster

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate…

Cited by 19 Source ↗PDF ↗Scholar ↗

2023

Continuous Spatiotemporal Transformer

Antonio Henrique de Oliveira Fonseca, Emanuele Zappala, Josue Ortega Caro, David van Dijk

ICML 2023poster

Modeling spatiotemporal dynamical systems is a fundamental challenge in machine learning. Transformer models have been very successful in NLP and computer vision where they provide interpretable representations of data. However, a limitation of transformers in modeling continuous dynamical systems i…

Cited by 11 Source ↗PDF ↗Scholar ↗