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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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| 2 |
Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Cross-Situational Learning Towards Robot Grounding
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In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
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Combining Bayesian and AI approaches for Autonomous Driving
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In: IROS 2021 - IEEE/RSJ International Conference on Intelligent Robots and Systems - Workshop "Perception and Navigation for Autonomous Robotics in Unstructured and Dynamic Environments" ; https://hal.inria.fr/hal-03518232 ; IROS 2021 - IEEE/RSJ International Conference on Intelligent Robots and Systems - Workshop "Perception and Navigation for Autonomous Robotics in Unstructured and Dynamic Environments", Sep 2021, Prague, Czech Republic (2021)
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Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning
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In: ITSC 2021 - 24th IEEE International Conference on Intelligent Transportation Systems ; https://hal.inria.fr/hal-03372856 ; ITSC 2021 - 24th IEEE International Conference on Intelligent Transportation Systems, Sep 2021, Indianapolis, United States. pp.1-7 (2021)
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"It Is Not the Robot Who Learns, It Is Me." Treating Severe Dysgraphia Using Child-Robot Interaction
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In: ISSN: 1664-0640 ; Frontiers in Psychiatry ; https://hal.sorbonne-universite.fr/hal-03152170 ; Frontiers in Psychiatry, Frontiers, 2021, 12, pp.596055. ⟨10.3389/fpsyt.2021.596055⟩ (2021)
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| 7 |
Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
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In: ISSN: 2162-237X ; IEEE Transactions on Neural Networks and Learning Systems ; https://hal.inria.fr/hal-03031413 ; IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2021, ⟨10.1109/TNNLS.2021.3095140⟩ ; https://ieeexplore.ieee.org/abstract/document/9548713/metrics#metrics (2021)
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| 8 |
Editorial: Language and Robotics
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In: ISSN: 2296-9144 ; Frontiers in Robotics and AI ; https://hal.inria.fr/hal-03533733 ; Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.674832⟩ (2021)
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| 9 |
Does the Goal Matter? Emotion Recognition Tasks Can Change the Social Value of Facial Mimicry towards Artificial Agents
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In: ISSN: 2296-9144 ; Frontiers in Robotics and AI ; https://hal.archives-ouvertes.fr/hal-03409678 ; Frontiers in Robotics and AI, Frontiers Media S.A., 2021, 8, ⟨10.3389/frobt.2021.699090⟩ (2021)
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| 10 |
From vocal prosody to movement prosody, from HRI to understanding humans
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In: VIHAR Vocal Interactivity in-and-between Humans, Animals and Robots ; https://hal.archives-ouvertes.fr/hal-03504825 ; VIHAR Vocal Interactivity in-and-between Humans, Animals and Robots, Oct 2021, Paris, France (2021)
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| 11 |
Comment les animaux et les robots transforment les relations ?
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In: Colloque international « Objets animés, humains, animaux : partenaires de soins tendres » ; https://hal.archives-ouvertes.fr/hal-03504819 ; Colloque international « Objets animés, humains, animaux : partenaires de soins tendres », Nov 2021, Grenoble, France (2021)
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| 12 |
Human communication and robotics
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In: VIHAR Vocal Interactivity in-and-between Humans, Animals and Robots ; https://hal.archives-ouvertes.fr/hal-03504822 ; VIHAR Vocal Interactivity in-and-between Humans, Animals and Robots, Oct 2021, Paris, France (2021)
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| 13 |
Insights into event representation from a sensorimotor model of event perception
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In: ICDL 2020 - 1st SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) workshop ; https://hal.archives-ouvertes.fr/hal-03202971 ; ICDL 2020 - 1st SMILES (Sensorimotor Interaction, Language and Embodiment of Symbols) workshop, Nov 2020, Valparaiso / Virtual, Chile ; https://sites.google.com/view/smiles-workshop/ (2020)
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| 14 |
Cross-Situational Learning with Reservoir Computing for Language Acquisition Modelling
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In: 2020 International Joint Conference on Neural Networks (IJCNN 2020) ; https://hal.inria.fr/hal-02594725 ; 2020 International Joint Conference on Neural Networks (IJCNN 2020), Jul 2020, Glasgow, Scotland, United Kingdom ; https://wcci2020.org/ (2020)
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| 15 |
Language Acquisition with Echo State Networks: Towards Unsupervised Learning
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In: ICDL 2020 - IEEE International Conference on Development and Learning ; https://hal.inria.fr/hal-02926613 ; ICDL 2020 - IEEE International Conference on Development and Learning, Oct 2020, Valparaiso / Virtual, Chile (2020)
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| 16 |
A Journey in ESN and LSTM Visualisations on a Language Task
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In: https://hal.inria.fr/hal-03030248 ; 2020 (2020)
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Abstract:
Echo States Networks (ESN) and Long-Short Term Memory networks (LSTM) are two popular architectures of Recurrent Neural Networks (RNN) to solve machine learning task involving sequential data. However, little have been done to compare their performances and their internal mechanisms on a common task. In this work, we trained ESNs and LSTMs on a Cross-Situationnal Learning (CSL) task. This task aims at modelling how infants learn language: they create associations between words and visual stimuli in order to extract meaning from words and sentences. The results are of three kinds: performance comparison, internal dynamics analyses and visualization of latent space. (1) We found that both models were able to successfully learn the task: the LSTM reached the lowest error for the basic corpus, but the ESN was quicker to train. Furthermore, the ESN was able to outperform LSTMs on datasets more challenging without any further tuning needed. (2) We also conducted an analysis of the internal units activations of LSTMs and ESNs. Despite the deep differences between both models (trained or fixed internal weights), we were able to uncover similar inner mechanisms: both put emphasis on the units encoding aspects of the sentence structure. (3) Moreover, we present Recurrent States Space Visualisations (RSSviz), a method to visualize the structure of latent state space of RNNs, based on dimension reduction (using UMAP). This technique enables us to observe a fractal embedding of sequences in the LSTM. RSSviz is also useful for the analysis of ESNs (i) to spot difficult examples and (ii) to generate animated plots showing the evolution of activations across learning stages. Finally, we explore qualitatively how the RSSviz could provide an intuitive visualisation to understand the influence of hyperparameters on the reservoir dynamics prior to ESN training.
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Keyword:
[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]; Cross-Situational Learning; Dimension Reduction; ESN; LSTM; UMAP; Visualisation
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URL: https://hal.inria.fr/hal-03030248
https://hal.inria.fr/hal-03030248/file/Comparison_between_LSTM_and_ESN%2812%29.pdf
https://hal.inria.fr/hal-03030248/document
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| 17 |
Mixing Bayesian and Artificial Intelligence approaches for Autonomous Driving
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In: Tech M&A 2019 - Minalogic Technical Conference ; https://hal.inria.fr/hal-02434275 ; Tech M&A 2019 - Minalogic Technical Conference, May 2019, Grenoble, France (2019)
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| 18 |
A journey in the history of Automated Driving
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In: IROS 2019 - IEEE/RSJ International Conference on Intelligent Robots and Systems ; https://hal.inria.fr/hal-02428196 ; IROS 2019 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Nov 2019, Macau, China. pp.1-27 (2019)
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| 19 |
Learning to Parse Grounded Language using Reservoir Computing
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In: ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics ; https://hal.inria.fr/hal-02422157 ; ICDL-Epirob 2019 - Joint IEEE 9th International Conference on Development and Learning and Epigenetic Robotics, Aug 2019, Olso, Norway. ⟨10.1109/devlrn.2019.8850718⟩ ; https://ieeexplore.ieee.org/abstract/document/8850718 (2019)
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| 20 |
Computer simulations of coupled idiosyncrasies in speech perception and speech production with COSMO, a perceptuo-motor Bayesian model of speech communication
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In: ISSN: 1932-6203 ; EISSN: 1932-6203 ; PLoS ONE ; https://hal.sorbonne-universite.fr/hal-01994708 ; PLoS ONE, Public Library of Science, 2019, 14 (1), pp.e0210302. ⟨10.1371/journal.pone.0210302⟩ (2019)
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