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Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling
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In: CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing ; https://hal.inria.fr/hal-02085093 ; CICLing 2019 - 20th International Conference on Computational Linguistics and Intelligent Text Processing, Apr 2019, La Rochelle, France (2019)
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Unsupervised Information Extraction: Regularizing Discriminative Approaches with Relation Distribution Losses
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In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics ; ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02318233 ; ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Jul 2019, Florence, Italy. pp.1378-1387, ⟨10.18653/v1/P19-1133⟩ ; http://www.acl2019.org (2019)
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Teach Your Robot Your Language! Trainable Neural Parser for Modelling Human Sentence Processing: Examples for 15 Languages
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In: ISSN: 2379-8920 ; EISSN: 2379-8939 ; IEEE Transactions on Cognitive and Developmental Systems ; https://hal.inria.fr/hal-01964541 ; IEEE Transactions on Cognitive and Developmental Systems, Institute of Electrical and Electronics Engineers, Inc, 2019, ⟨10.1109/TCDS.2019.2957006⟩ ; https://doi.org/10.1109/tcds.2019.2957006 (2019)
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Speech Emotion Recognition: Recurrent Neural Networks compared to SVM and Linear Regression
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In: Artificial Neural Networks and Machine Learning – ICANN 2017 ; https://hal.archives-ouvertes.fr/hal-02432632 ; Alessandra Lintas; Stefano Rovetta; Paul F.M.J. Verschure; Alessandro E.P. Villa. Artificial Neural Networks and Machine Learning – ICANN 2017, 10613, Springer International Publishing, pp.451-453, 2019, Lecture Notes in Computer Science ; https://link.springer.com/book/10.1007%2F978-3-319-68600-4?page=2#toc (2019)
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A Reservoir Model for Intra-Sentential Code-Switching Comprehension in French and English
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In: CogSci'19 - 41st Annual Meeting of the Cognitive Science Society ; https://hal.inria.fr/hal-02432831 ; CogSci'19 - 41st Annual Meeting of the Cognitive Science Society, Jul 2019, Montréal, Canada ; https://cognitivesciencesociety.org/cogsci-2019/ (2019)
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Unsupervised Machine Learning & Prediction of Latent Structures Using an Enhanced Bi-LSTM Model for Writing Normalisation
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In: CIDE 2019 ; https://hal-cnam.archives-ouvertes.fr/hal-02476675 ; CIDE 2019, Apr 2019, Djerba, Tunisia (2019)
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Learning Latent Trees with Stochastic Perturbations and Differentiable Dynamic Programming
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In: 57th annual meeting of Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03029255 ; 57th annual meeting of Association for Computational Linguistics, Jul 2019, Florence, Italy (2019)
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Self-Educated Language Agent With Hindsight Experience Replay For Instruction Following
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In: https://hal.archives-ouvertes.fr/hal-02386585 ; 2019 (2019)
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A novel approach based on clustering along with feature maximization and contrast graphs. An example of application for diachronic analysis of research
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In: CICLing 2019 (20th International Conference on Computational Linguistics and Intelligent Text Processing) ; https://hal.inria.fr/hal-03179712 ; CICLing 2019 (20th International Conference on Computational Linguistics and Intelligent Text Processing), Apr 2019, La Rochelle, France (2019)
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Shaping representations through communication: community size effect in artificial learning systems ...
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Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation ...
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Consistency by Agreement in Zero-shot Neural Machine Translation ...
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Multi-lingual Dialogue Act Recognition with Deep Learning Methods ...
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Modeling neural dynamics during speech production using a state space variational autoencoder ...
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Abstract:
Characterizing the neural encoding of behavior remains a challenging task in many research areas due in part to complex and noisy spatiotemporal dynamics of evoked brain activity. An important aspect of modeling these neural encodings involves separation of robust, behaviorally relevant signals from background activity, which often contains signals from irrelevant brain processes and decaying information from previous behavioral events. To achieve this separation, we develop a two-branch State Space Variational AutoEncoder (SSVAE) model to individually describe the instantaneous evoked foreground signals and the context-dependent background signals. We modeled the spontaneous speech-evoked brain dynamics using smoothed Gaussian mixture models. By applying the proposed SSVAE model to track ECoG dynamics in one participant over multiple hours, we find that the model can predict speech-related dynamics more accurately than other latent factor inference algorithms. Our results demonstrate that separately ... : 5 pages ...
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Keyword:
FOS Computer and information sciences; Human-Computer Interaction cs.HC; Machine Learning cs.LG; Neural and Evolutionary Computing cs.NE
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URL: https://dx.doi.org/10.48550/arxiv.1901.04024 https://arxiv.org/abs/1901.04024
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BowNet: Dilated Convolution Neural Network for Ultrasound Tongue Contour Extraction ...
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Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison ...
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Error Analysis for Vietnamese Named Entity Recognition on Deep Neural Network Models ...
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The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction ...
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AeGAN: Time-Frequency Speech Denoising via Generative Adversarial Networks ...
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