| 1 |
Automatic Speech Recognition and Query By Example for Creole Languages Documentation
|
|
|
|
In: Findings of the Association for Computational Linguistics: ACL 2022 ; https://hal.archives-ouvertes.fr/hal-03625303 ; Findings of the Association for Computational Linguistics: ACL 2022, May 2022, Dublin, Ireland (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Cross-Situational Learning Towards Robot Grounding
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Cross-Situational Learning Towards Robot Grounding
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
End-to-end speaker segmentation for overlap-aware resegmentation
|
|
|
|
In: Interspeech 2021 ; https://hal-univ-lemans.archives-ouvertes.fr/hal-03257524 ; Interspeech 2021, Aug 2021, Brno, Czech Republic ; https://www.interspeech2021.org/ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
High-resolution speaker counting in reverberant rooms using CRNN with Ambisonics features
|
|
|
|
In: EUSIPCO 2020 - 28th European Signal Processing Conference (EUSIPCO) ; https://hal.archives-ouvertes.fr/hal-03537323 ; EUSIPCO 2020 - 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. pp.71-75, ⟨10.23919/Eusipco47968.2020.9287637⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Tackling Morphological Analogies Using Deep Learning -- Extended Version
|
|
|
|
In: https://hal.inria.fr/hal-03425776 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
|
|
|
|
In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
What does the Canary Say? Low-Dimensional GAN Applied to Birdsong
|
|
|
|
In: https://hal.inria.fr/hal-03244723 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
Artificial Text Detection via Examining the Topology of Attention Maps
|
|
|
|
In: ACL Anthology ; Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-03456191 ; Empirical Methods in Natural Language Processing, ACL (Association for Computational Linguistics), Nov 2021, Punta Cana, Dominican Republic (2021)
|
|
BASE
|
|
Show details
|
|
|
| 11 |
Modeling the neural network responsible for song learning ; Modélisation du réseau neuronal responsable de l'apprentissage du chant chez l'oiseau chanteur
|
|
|
|
In: https://tel.archives-ouvertes.fr/tel-03217834 ; Modeling and Simulation. Université de Bordeaux, 2021. English. ⟨NNT : 2021BORD0107⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Multimodal Coarticulation Modeling : Towards the animation of an intelligible talking head ; Modélisation de la coarticulation multimodale : vers l'animation d'une tête parlante intelligible
|
|
|
|
In: https://hal.univ-lorraine.fr/tel-03203815 ; Intelligence artificielle [cs.AI]. Université de Lorraine, 2021. Français. ⟨NNT : 2021LORR0019⟩ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Impact of Segmentation and Annotation in French end-to-end Synthesis
|
|
|
|
In: Proc. 11th ISCA Speech Synthesis Workshop (SSW 11) ; SSW 11th ISCA Speech Synthesis Workshop ; https://hal.archives-ouvertes.fr/hal-03362000 ; SSW 11th ISCA Speech Synthesis Workshop, Aug 2021, Budapest, Hungary. pp.13-18, ⟨10.21437/SSW.2021-3⟩ ; https://ssw11.hte.hu/ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
|
|
|
|
In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203318 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia (2021)
|
|
Abstract:
International audience ; In learning systems, hyperparameters are parameters that are not learned but need to be set a priori. In Reservoir Computing, there are several parameters that needs to be set a priori depending on the task. Newcomers to Reservoir Computing cannot have a good intuition on which hyperparameters to tune and how to tune them. For instance, beginners often explore the reservoir sparsity, but in practice this parameter is not of high influence on performance for ESNs. Most importantly, many authors keep doing suboptimal hyperparameter searches: using grid search as a tool to explore more than two hyperparameters, while restraining the spectral radius to be below unity. In this short paper, we give some suggestions, intuitions, and give a general method to find robust hyperparameters while understanding their influence on perfor- mance. We also provide a graphical interface (included in ReservoirPy) in order to make this hyperparameter search more intuitive. Finally, we discuss some potential refinements of the proposed method.
|
|
Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]; [SCCO.LING]Cognitive science/Linguistics; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]; Echo State Networks; Effective Spectral Radius; Grid Search; Hyperparameters; Random Search; Reservoir Computing
|
|
URL: https://hal.inria.fr/hal-03203318v2/document
https://hal.inria.fr/hal-03203318
https://hal.inria.fr/hal-03203318v2/file/Hinaut2021_ICANN_Reservoir-Random-Search_HAL-preprint-v2.pdf
|
|
BASE
|
|
Hide details
|
|
|
| 15 |
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
|
|
|
|
In: https://hal.inria.fr/hal-03203374 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Which Hype for my New Task? Hints and Random Search for Reservoir Computing Hyperparameters
|
|
|
|
In: https://hal.inria.fr/hal-03203318 ; 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs
|
|
|
|
In: ICANN 2021 - 30th International Conference on Artificial Neural Networks ; https://hal.inria.fr/hal-03203374 ; ICANN 2021 - 30th International Conference on Artificial Neural Networks, Sep 2021, Bratislava, Slovakia. pp.71--82, ⟨10.1007/978-3-030-86383-8_6⟩ ; https://link.springer.com/chapter/10.1007/978-3-030-86383-8_6 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
|
|
|
|
In: IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03003469 ; IEEE Spoken Language Technology Workshop, Jan 2021, Virtual, China (2021)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
Hierarchical-Task Reservoir for Online Semantic Analysis from Continuous Speech
|
|
|
|
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)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
Recognizing lexical units in low-resource language contexts with supervised and unsupervised neural networks
|
|
|
|
In: https://hal.archives-ouvertes.fr/hal-03429051 ; [Research Report] LACITO (UMR 7107). 2021 (2021)
|
|
BASE
|
|
Show details
|
|
|
|
|
