Bayesian robot programming O Lebeltel, P Bessiere, J Diard, E Mazer Autonomous Robots 16 (1), 49-79, 2004 | 258 | 2004 |
Dynamical variational autoencoders: A comprehensive review L Girin, S Leglaive, X Bie, J Diard, T Hueber, X Alameda-Pineda Foundations and Trends in Machine Learning 15 (1-2), 1-175, 2020 | 221 | 2020 |
Common Bayesian models for common cognitive issues F Colas, J Diard, P Bessiere Acta biotheoretica 58, 191-216, 2010 | 96 | 2010 |
Enhancing reading performance through action video games: The role of visual attention span A Antzaka, M Lallier, S Meyer, J Diard, M Carreiras, S Valdois Scientific reports 7 (1), 14563, 2017 | 79 | 2017 |
Adverse conditions improve distinguishability of auditory, motor, and perceptuo-motor theories of speech perception: An exploratory Bayesian modelling study C Moulin-Frier, R Laurent, P Bessière, JL Schwartz, J Diard Speech Recognition in Adverse Conditions, 288-311, 2013 | 79 | 2013 |
COSMO (“Communicating about Objects using Sensory–Motor Operations”): A Bayesian modeling framework for studying speech communication and the emergence of phonological systems C Moulin-Frier, J Diard, JL Schwartz, P Bessière Journal of Phonetics 53, 5-41, 2015 | 65 | 2015 |
Bayesian action–perception computational model: Interaction of production and recognition of cursive letters E Gilet, J Diard, P Bessiere PloS one 6 (6), e20387, 2011 | 47 | 2011 |
Optimal speech motor control and token-to-token variability: a Bayesian modeling approach JF Patri, J Diard, P Perrier Biological cybernetics 109, 611-626, 2015 | 43 | 2015 |
Modeling the length effect for words in lexical decision: The role of visual attention E Ginestet, T Phénix, J Diard, S Valdois Vision research 159, 10-20, 2019 | 41 | 2019 |
The complementary roles of auditory and motor information evaluated in a Bayesian perceptuo-motor model of speech perception. R Laurent, ML Barnaud, JL Schwartz, P Bessière, J Diard Psychological review 124 (5), 572, 2017 | 32 | 2017 |
What drives the perceptual change resulting from speech motor adaptation? Evaluation of hypotheses in a Bayesian modeling framework JF Patri, P Perrier, JL Schwartz, J Diard PLoS computational biology 14 (1), e1005942, 2018 | 29 | 2018 |
La carte bayésienne: un modèle probabiliste hiérarchique pour la navigation en robotique mobile J Diard Institut National Polytechnique de Grenoble-INPG, 2003 | 28 | 2003 |
Automatized set-up procedure for transcranial magnetic stimulation protocols S Harquel, J Diard, E Raffin, B Passera, G Dall'Igna, C Marendaz, O David, ... Neuroimage 153, 307-318, 2017 | 23 | 2017 |
Les modèles computationnels de lecture T Phénix, J Diard, S Valdois Traité de neurolinguistique, 167-182, 2016 | 22 | 2016 |
Reanalyzing neurocognitive data on the role of the motor system in speech perception within COSMO, a Bayesian perceptuo-motor model of speech communication ML Barnaud, P Bessière, J Diard, JL Schwartz Brain and language 187, 19-32, 2018 | 20 | 2018 |
Proxemics models for human-aware navigation in robotics: Grounding interaction and personal space models in experimental data from psychology ML Barnaud, N Morgado, R Palluel-Germain, J Diard, A Spalanzani Proceedings of the 3rd IROS’2014 workshop “Assistance and Service Robotics …, 2014 | 19 | 2014 |
Computer simulations of coupled idiosyncrasies in speech perception and speech production with COSMO, a perceptuo-motor Bayesian model of speech communication ML Barnaud, JL Schwartz, P Bessière, J Diard PloS one 14 (1), e0210302, 2019 | 16 | 2019 |
Modeling sensory preference in speech motor planning: a Bayesian modeling framework JF Patri, J Diard, P Perrier Frontiers in Psychology 10, 2339, 2019 | 15 | 2019 |
Bayesian programming and hierarchical learning in robotics J Diard, O Lebeltel SAB2000 Proceedings Supplement Book, 10pages, 2000 | 15 | 2000 |
Probabilistic modeling of orthographic learning based on visuo-attentional dynamics E Ginestet, S Valdois, J Diard Psychonomic Bulletin & Review 29 (5), 1649-1672, 2022 | 14 | 2022 |