| 1 |
Imputing Out-of-Vocabulary Embeddings with LOVE Makes Language Models Robust with Little Cost
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Imputing out-of-vocabulary embeddings with LOVE makes language models robust with little cost
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Population modeling with machine learning can enhance measures of mental health
|
|
|
|
In: ISSN: 2047-217X ; GigaScience ; https://hal.inria.fr/hal-03470466 ; GigaScience, BioMed Central, 2021, ⟨10.1101/2020.08.25.266536⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Population modeling with machine learning can enhance measures of mental health
|
|
|
|
In: Gigascience (2021)
|
|
Abstract:
BACKGROUND: Biological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention? RESULTS: Here, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data. CONCLUSION: Population modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.
|
|
Keyword:
Research
|
|
URL: https://doi.org/10.1093/gigascience/giab071
http://www.ncbi.nlm.nih.gov/pubmed/34651172
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8559220/
|
|
BASE
|
|
Hide details
|
|
|
| 5 |
Exploring the anatomical encoding of voice with a mathematical model of the vocal system.
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm
|
|
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Identification of Mood-Relevant Brain Connections Using a Continuous, Subject-Driven Rumination Paradigm.
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
API design for machine learning software: experiences from the scikit-learn project
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
Decoding Visual Percepts Induced by Word Reading with fMRI
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
|
|
