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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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Abstract:
Rumination, an internal cognitive state characterized by recursive thinking of current self-distress and past negative events, has been found to correlate with the development of depressive disorders. Here, we investigated the feasibility of using connectivity for distinguishing different emotional states induced by a novel free-streaming, subject-driven experimental paradigm. Connectivity between 78 functional regions of interest (ROIs) within 14 large-scale networks and 6 structural ROIs particularly relevant to emotional processing were used for classifying 4 mental states in 19 healthy controls. The 4 mental states comprised: An unconstrained period of mind wandering; a ruminative mental state self-induced by recalling a time of personal disappointment; a euphoric mental state self-induced by recalling what brings the subject joy; and a sequential episodic recollection of the events of the day. A support vector machine achieved accuracies ranging from 89% to 94% in classifying pairs of different mental states. We reported the most significant brain connections that best discriminated these mental states. In particular, connectivity changes involving the amygdala were found to be important for distinguishing the rumination condition from the other mental states. Our results demonstrated that connectivity-based classification of subject-driven emotional states constitutes a novel and effective approach for studying ruminative behavior.
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Original Articles
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URL: https://doi.org/10.1093/cercor/bhu255
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737600/
http://www.ncbi.nlm.nih.gov/pubmed/25331601
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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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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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