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Evaluation of Tacotron Based Synthesizers for Spanish and Basque
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In: Applied Sciences; Volume 12; Issue 3; Pages: 1686 (2022)
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| 2 |
CCG Supertagging as Top-down Tree Generation
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In: Proceedings of the Society for Computation in Linguistics (2021)
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| 3 |
Vowel Harmony Viewed as Error-Correcting Code
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In: Proceedings of the Society for Computation in Linguistics (2021)
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| 4 |
Generating Adversarial Examples for Topic-dependent Argument Classification
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In: COMMA 2020 - 8th International Conference on Computational Models of Argument ; https://hal.archives-ouvertes.fr/hal-02933266 ; COMMA 2020 - 8th International Conference on Computational Models of Argument, Sep 2020, Perugia, Italy (2020)
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| 7 |
Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech
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| 8 |
Complexity of Stability
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In: Leibniz International Proceedings in Informatics, 181 ; 31st International Symposium on Algorithms and Computation (ISAAC 2020) (2020)
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| 9 |
NAT: Noise-Aware Training for Robust Neural Sequence Labeling
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In: Fraunhofer IAIS (2020)
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| 10 |
VoiceHome-2, an extended corpus for multichannel speech processing in real homes
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.inria.fr/hal-01923108 ; Speech Communication, Elsevier : North-Holland, 2019, 106, pp.68-78. ⟨10.1016/j.specom.2018.11.002⟩ (2019)
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| 11 |
Towards Interpretability and Robustness of Machine Learning Models
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Chen, Jianbo. - : eScholarship, University of California, 2019
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Abstract:
Modern machine learning models can be difficult to probe and understand after they have been trained. This is a major problem for the field, with consequences for trustworthiness, diagnostics, debugging, robustness, and a range of other engineering and human interaction issues surrounding the deployment of a model. Another problem of modern machine learning models is their vulnerability to small adversarial perturbations to the input, which incurs a security risk when they are applied to critical areas.In this thesis, we develop systematic and efficient tools for interpreting machine learning models and evaluating their adversarial robustness. Part I focuses on model interpretation. We derive an efficient feature scoring method by exploiting the graph structure in data. We also develop a learning-based method under an information-based framework. As an attempt to leverage prior knowledge about what constitutes a satisfying interpretation in a given domain, we propose a systematic approach to exploiting syntactic constituency structure by leveraging a parse tree for interpretation of models in the setting of linguistic data. Part II focuses on the evaluation of adversarial robustness. We first propose a probabilistic framework for generating adversarial examples on discrete data, and develop two algorithms to implement it. We also introduce a novel attack method in the setting where the attacker has access to model decisions alone. We investigate the robustness of various machine learning models and existing defense mechanisms under the proposed attack method. In Part III, we build a connection between the two fields by developing a method for detecting adversarial examples via tools in model interpretation.
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Keyword:
adversarial robustness; Artificial intelligence; Computer science; model interpretation; Statistics
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URL: https://escholarship.org/uc/item/2bj9c0br
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| 12 |
Assessing the Robustness of Conversational Agents using Paraphrases
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| 13 |
Robust speech recognition for german and dialectal broadcast programmes
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In: Fraunhofer IAIS (2018)
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| 14 |
Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online Handwritten Chinese Text Recognition
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| 15 |
Lightweight Spoken Utterance Classification with CFG, tf-idf and Dynamic Programming
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In: ISBN: 978-3-319-68455-0 ; Statistical Language and Speech Processing (SLSP) pp. 143-154 (2017)
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| 16 |
A French corpus for distant-microphone speech processing in real homes
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In: Interspeech 2016 ; https://hal.inria.fr/hal-01343060 ; Interspeech 2016, Sep 2016, San Francisco, United States (2016)
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| 17 |
Reconnaissance automatique de gestes manuels en langue des signes
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In: RFIA 2016 ; RFIA'16: Le vingtième congrès national sur la Reconnaissance des Formes et l'Intelligence Artificielle ; https://hal.archives-ouvertes.fr/hal-01332141 ; RFIA'16: Le vingtième congrès national sur la Reconnaissance des Formes et l'Intelligence Artificielle , Jun 2016, Clermont-Ferrand, France (2016)
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| 18 |
Investigation of Back-off Based Interpolation Between Recurrent Neural Network and N-gram Language Models (Author's Manuscript)
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| 19 |
Lexicographic α-robustness: an application to the 1-median problem
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