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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 Hits 1 – 20 of 41 1 Neural reality of argument structure constructions ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2022 BASE Show details 2 On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ... Berlot-Attwell, Ian; Rudzicz, Frank. - : arXiv, 2021 BASE Show details 3 TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction ... Li, Bai; Rudzicz, Frank. - : arXiv, 2021 BASE Show details 4 Quantifying the Task-Specific Information in Text-Based Classifications ... Zhu, Zining; Balagopalan, Aparna; Ghassemi, Marzyeh. - : arXiv, 2021 BASE Show details 5 An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hirst, Graeme; Rudzicz, Frank. - : Underline Science Inc., 2021 BASE Show details 6 How is BERT surprised? Layerwise detection of linguistic anomalies ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2021 BASE Show details 7 Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech Balagopalan, Aparna; Eyre, Benjamin; Robin, Jessica... In: Front Aging Neurosci (2021) BASE Show details 8 Identification of primary and collateral tracks in stuttered speech Riad, Rachid; Bachoud-Lévi, Anne-Catherine; Rudzicz, Frank... In: LREC 2020 - 12th Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-02959454 ; LREC 2020 - 12th Conference on Language Resources and Evaluation, May 2020, Marseille, France (2020) BASE Show details 9 Semantic coordinates analysis reveals language changes in the AI field ... Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 10 Word class flexibility: A deep contextualized approach ... Li, Bai; Thomas, Guillaume; Xu, Yang. - : arXiv, 2020 BASE Show details 11 To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ... Balagopalan, Aparna; Eyre, Benjamin; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 12 An information theoretic view on selecting linguistic probes ... Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 13 Examining the rhetorical capacities of neural language models ... Zhu, Zining; Pan, Chuer; Abdalla, Mohamed. - : arXiv, 2020 BASE Show details 14 A textual analysis of US corporate social responsibility reports Clarkson, Peter M.; Ponn, Jordan; Richardson, Gordon D.. - : Wiley-Blackwell, 2020 BASE Show details 15 Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ... Novikova, Jekaterina; Balagopalan, Aparna; Shkaruta, Ksenia. - : arXiv, 2019 BASE Show details 16 Representation Learning for Discovering Phonemic Tone Contours ... Li, Bai; Xie, Jing Yi; Rudzicz, Frank. - : arXiv, 2019 BASE Show details 17 Machine learning for MEG during speech tasks Kostas, Demetres; Pang, Elizabeth W.; Rudzicz, Frank. - : Nature Publishing Group UK, 2019 Abstract: We consider whether a deep neural network trained with raw MEG data can be used to predict the age of children performing a verb-generation task, a monosyllable speech-elicitation task, and a multi-syllabic speech-elicitation task. Furthermore, we argue that the network makes predictions on the grounds of differences in speech development. Previous work has explored taking ‘deep’ neural networks (DNNs) designed for, or trained with, images to classify encephalographic recordings with some success, but this does little to acknowledge the structure of these data. Simple neural networks have been used extensively to classify data expressed as features, but require extensive feature engineering and pre-processing. We present novel DNNs trained using raw magnetoencephalography (MEG) and electroencephalography (EEG) recordings that mimic the feature-engineering pipeline. We highlight criteria the networks use, including relative weighting of channels and preferred spectro-temporal characteristics of re-weighted channels. Our data feature 92 subjects aged 4–18, recorded using a 151-channel MEG system. Our proposed model scores over 95% mean cross-validation accuracy distinguishing above and below 10 years of age in single trials of un-seen subjects, and can classify publicly available EEG with state-of-the-art accuracy. Keyword: Article URL: https://doi.org/10.1038/s41598-019-38612-9 http://www.ncbi.nlm.nih.gov/pubmed/30733596 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367450/ BASE Hide details 18 The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ... Balagopalan, Aparna; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 19 Detecting cognitive impairments by agreeing on interpretations of linguistic features ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 20 Deconfounding age effects with fair representation learning when assessing dementia ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details Page: 1 2 3

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