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Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
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In: ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03613101 ; ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, May 2022, Dublin, Ireland (2022)
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Population modeling with machine learning can enhance measures of mental health
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In: ISSN: 2047-217X ; GigaScience ; https://hal.inria.fr/hal-03470466 ; GigaScience, BioMed Central, 2021, ⟨10.1101/2020.08.25.266536⟩ (2021)
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Population modeling with machine learning can enhance measures of mental health
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In: Gigascience (2021)
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Exploring the anatomical encoding of voice with a mathematical model of the vocal system.
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In: ISSN: 1053-8119 ; EISSN: 1095-9572 ; NeuroImage ; https://hal.inria.fr/hal-01498364 ; NeuroImage, Elsevier, 2016, 141, pp.31-9. ⟨10.1016/j.neuroimage.2016.07.033⟩ (2016)
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Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm
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Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm.
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In: ISSN: 1047-3211 ; EISSN: 1460-2199 ; Cerebral Cortex ; https://hal.inria.fr/hal-01094759 ; Cerebral Cortex, Oxford University Press (OUP), 2014, pp.12 (2014)
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| 8 |
API design for machine learning software: experiences from the scikit-learn project
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In: European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases ; https://hal.inria.fr/hal-00856511 ; European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Databases, Sep 2013, Prague, Czech Republic (2013)
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| 9 |
Decoding Visual Percepts Induced by Word Reading with fMRI
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In: Pattern Recognition in NeuroImaging (PRNI), 2012 International Workshop on ; https://hal.inria.fr/hal-00730768 ; Pattern Recognition in NeuroImaging (PRNI), 2012 International Workshop on, Jul 2012, Londres, United Kingdom. pp.13-16, ⟨10.1109/PRNI.2012.20⟩ ; http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6295916&tag=1 (2012)
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Abstract:
International audience ; Word reading involves multiple cognitive processes. To infer which word is being visualized, the brain first processes the visual percept, deciphers the letters, bigrams, and activates different words based on context or prior expectation like word frequency. In this contribution, we use supervised machine learning techniques to decode the first step of this processing stream using functional Magnetic Resonance Images (fMRI). We build a decoder that predicts the visual percept formed by four letter words, allowing us to identify words that were not present in the training data. To do so, we cast the learning problem as multiple classification problems after describing words with multiple binary attributes. This work goes beyond the identification or reconstruction of single letters or simple geometrical shapes and addresses a challenging estimation problem, that is the prediction of multiple variables from a single observation, hence facing the problem of learning multiple predictors from correlated inputs.
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Keyword:
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging; brain reading; classification; decoding; fMRI; machine learning
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URL: https://doi.org/10.1109/PRNI.2012.20
https://hal.inria.fr/hal-00730768/file/paper.pdf
https://hal.inria.fr/hal-00730768/document
https://hal.inria.fr/hal-00730768
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