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The genetic architecture of language functional connectivity
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In: ISSN: 1053-8119 ; EISSN: 1095-9572 ; NeuroImage ; https://hal.sorbonne-universite.fr/hal-03566120 ; NeuroImage, Elsevier, 2022, 249, pp.118795. ⟨10.1016/j.neuroimage.2021.118795⟩ (2022)
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The "Fat Face" illusion: A robust adaptation for processing pairs of faces
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In: ISSN: 0042-6989 ; EISSN: 0042-6989 ; Vision Research ; https://hal.archives-ouvertes.fr/hal-03579276 ; Vision Research, Elsevier, 2022, 195, pp.108015. ⟨10.1016/j.visres.2022.108015⟩ (2022)
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From Biological Synapses to “Intelligent” Robots
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In: ISSN: 2079-9292 ; Electronics ; https://hal.archives-ouvertes.fr/hal-03590998 ; Electronics, MDPI, 2022, 11 (5), pp.707. ⟨10.3390/electronics11050707⟩ (2022)
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Biological constraints on configural odour mixture perception
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In: ISSN: 0022-0949 ; EISSN: 1477-9145 ; Journal of Experimental Biology ; https://hal-cnrs.archives-ouvertes.fr/hal-03610253 ; Journal of Experimental Biology, The Company of Biologists, 2022, 225 (6), pp.jeb242274. ⟨10.1242/jeb.242274⟩ ; https://journals.biologists.com/jeb/article-abstract/225/6/jeb242274/274695/Biological-constraints-on-configural-odour-mixture (2022)
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Meta-Analysis of the Functional Neuroimaging Literature with Probabilistic Logic Programming
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In: https://hal.archives-ouvertes.fr/hal-03590714 ; 2022 (2022)
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Efficient localization of the cortical language network and its functional neuroanatomy in dyslexia
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Anxious voice and avoidant language in interaction with a woman wearing an Islamic headscarf: field-experimental evidence from the Paris metro
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In: https://hal.archives-ouvertes.fr/hal-03140246 ; 2022 (2022)
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| 8 |
Anxious voice and avoidant language in interaction with a woman wearing an Islamic headscarf: field-experimental evidence from the Paris metro
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In: https://hal.archives-ouvertes.fr/hal-03140246 ; 2022 (2022)
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| 9 |
A lexical approach for identifying behavioural action sequences
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In: ISSN: 1553-734X ; EISSN: 1553-7358 ; PLoS Computational Biology ; https://hal.sorbonne-universite.fr/hal-03521462 ; PLoS Computational Biology, Public Library of Science, 2022, 18 (1), pp.e1009672. ⟨10.1371/journal.pcbi.1009672⟩ (2022)
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| 10 |
Hippocampal and auditory contributions to speech segmentation
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In: ISSN: 0010-9452 ; Cortex ; https://hal.archives-ouvertes.fr/hal-03604957 ; Cortex, Elsevier, 2022, ⟨10.1016/j.cortex.2022.01.017⟩ (2022)
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| 11 |
Évaluation de la perception des sons de parole chez les populations pédiatriques : réflexion sur les épreuves existantes
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In: ISSN: 0298-6477 ; EISSN: 2117-7155 ; Glossa ; https://hal.archives-ouvertes.fr/hal-03646757 ; Glossa, UNADREO - Union NAtionale pour le Développement de la Recherche en Orthophonie, 2022, 132, pp.1-27 ; https://www.glossa.fr/index.php/glossa/article/view/1043 (2022)
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| 12 |
Inter-Individual Variability in Dorsal Stream Dynamics During Word Production
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In: https://hal.archives-ouvertes.fr/hal-03629184 ; 2022 (2022)
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| 13 |
"What It Means to Discriminate". Presentation of The Volume ; « Ce que discriminer veut dire ». Présentation du volume
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In: ISSN: 1638-5748 ; EISSN: 1638-573X ; CORELA - COgnition, REprésentation, LAngage ; https://halshs.archives-ouvertes.fr/halshs-03648818 ; CORELA - COgnition, REprésentation, LAngage, CERLICO-Cercle Linguistique du Centre et de l'Ouest (France), 2022, ⟨10.4000/corela.14510⟩ (2022)
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| 14 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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| 15 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Abstract:
How do children acquire language through unsupervised or noisy supervision? How do their brain process language? We take this perspective to machine learning and robotics, where part of the problem is understanding how language models can perform grounded language acquisition through noisy supervision and discussing how they can account for brain learning dynamics. Most prior works have tracked the co-occurrence between single words and referents to model how infants learn wordreferent mappings. This paper studies cross-situational learning (CSL) with full sentences: we want to understand brain mechanisms that enable children to learn mappings between words and their meanings from full sentences in early language learning. We investigate the CSL task on a few training examples with two sequence-based models: (i) Echo State Networks (ESN) and (ii) Long-Short Term Memory Networks (LSTM). Most importantly, we explore several word representations including One-Hot, GloVe, pretrained BERT, and fine-tuned BERT representations (last layer token representations) to perform the CSL task. We apply our approach to three diverse datasets (two grounded language datasets and a robotic dataset) and observe that (1) One-Hot, GloVe, and pretrained BERT representations are less efficient when compared to representations obtained from fine-tuned BERT. (2) ESN online with final learning (FL) yields superior performance over ESN online continual learning (CL), offline learning, and LSTMs, indicating the more biological plausibility of ESNs and the cognitive process of sentence reading. (2) LSTM with fewer hidden units showcases higher performance for small datasets, but LSTM with more hidden units is Cross-Situational Learning needed to perform reasonably well on larger corpora. (4) ESNs demonstrate better generalization than LSTM models for increasingly large vocabularies. Overall, these models are able to learn from scratch to link complex relations between words and their corresponding meaning concepts, handling polysemous and synonymous words. Moreover, we argue that such models can extend to help current human-robot interaction studies on language grounding and better understand children's developmental language acquisition. We make the code publicly available * .
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO]; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; BERT; cross-situational learning; echo state networks; grounded language; LSTM
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URL: https://hal.archives-ouvertes.fr/hal-03628290/document
https://hal.archives-ouvertes.fr/hal-03628290/file/Journal_of_Social_and_Robotics.pdf
https://hal.archives-ouvertes.fr/hal-03628290
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| 16 |
Rhythmic tapping difficulties in adults who stutter: a deficit in Central Clock and/or Motor Implementation?
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In: https://hal.archives-ouvertes.fr/hal-03508535 ; 2022 (2022)
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| 17 |
Vocal size exaggeration may have contributed to the origins of vocalic complexity
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In: ISSN: 0962-8436 ; EISSN: 1471-2970 ; Philosophical Transactions of the Royal Society B: Biological Sciences ; https://hal.archives-ouvertes.fr/hal-03501105 ; Philosophical Transactions of the Royal Society B: Biological Sciences, Royal Society, The, 2022, 377 (1841), ⟨10.1098/rstb.2020.0401⟩ (2022)
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| 18 |
Chapter 11. Consumer opinion about smoked bacon using Twitter and textual analysis: The challenge continues
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In: Sensory Analysis for the Development of Meat Products ; https://hal-agrosup-dijon.archives-ouvertes.fr/hal-03575175 ; Sensory Analysis for the Development of Meat Products, Elsevier, pp.181-196, 2022, 9780128228326. ⟨10.1016/B978-0-12-822832-6.00013-8⟩ (2022)
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| 19 |
РЕПРЕЗЕНТАЦИЯ КОНЦЕПТА «СЧАСТЬЕ» В РУССКОЯЗЫЧНЫХ ПРОИЗВЕДЕНИЯХ ЮМОРИСТИЧЕСКИХ ЖАНРОВ ... : REPRESENTATION OF THE CONCEPT “HAPPINESS” IN RUSSIAN PRECEDENT TEXTS OF COMIC GENRES ...
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Improving mathematics performance in 7-year-old children: Training the mapping from estimated quantities to Arabic digits ...
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