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The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models.

A. Goldstein, E. Ham, M. Schain, S. Nastase, Z. Zada, A. Dabush, B. Aubrey, H. Gazula, A. Feder, W. Doyle, S. Devore, P. Dugan, D. Friedman, R. Reichart, M. Brenner, A. Hassidim, O. Devinsky, A. Flinker, O. Levy, and U. Hasson. CoRR, (2023)

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An Intracranial Electrophysiology Study of Visual Language Encoding: The Contribution of the Precentral Gyrus to Silent Reading.E. Kaestner, T. Thesen, O. Devinsky, W. Doyle, C. Carlson, and E. Halgren. J. Cogn. Neurosci., 33 (11): 2197-2214 (2021)Hierarchical Conditional Random Fields for Outlier Detection: An Application to Detecting Epileptogenic Cortical Malformations.B. Ahmed, T. Thesen, K. Blackmon, Y. Zhao, O. Devinsky, R. Kuzniecky, and C. Brodley. ICML, volume 32 of JMLR Workshop and Conference Proceedings, page 1080-1088. JMLR.org, (2014)A neural speech decoding framework leveraging deep learning and speech synthesis.X. Chen, R. Wang, A. Khalilian-Gourtani, L. Yu, P. Dugan, D. Friedman, W. Doyle, O. Devinsky, Y. Wang, and A. Flinker. Nat. Mac. Intell., 6 (4): 467-480 (2024)A Non-parametric Approach to Detect Epileptogenic Lesions using Restricted Boltzmann Machines.Y. Zhao, B. Ahmed, T. Thesen, K. Blackmon, J. Dy, C. Brodley, R. Kuzniecky, and O. Devinsky. KDD, page 373-382. ACM, (2016)Decrypting "Cryptogenic" Epilepsy: Semi-supervised Hierarchical Conditional Random Fields For Detecting Cortical Lesions In MRI-Negative Patients.B. Ahmed, T. Thesen, K. Blackmon, R. Kuzniecky, O. Devinsky, and C. Brodley. J. Mach. Learn. Res., (2016)The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models.A. Goldstein, E. Ham, M. Schain, S. Nastase, Z. Zada, A. Dabush, B. Aubrey, H. Gazula, A. Feder, W. Doyle and 10 other author(s). CoRR, (2023)Neural correlates to automatic behavior estimations from RGB-D video in epilepsy unit.P. Gabriel, W. Doyle, O. Devinsky, D. Friedman, T. Thesen, and V. Gilja. EMBC, page 3402-3405. IEEE, (2016)Hidden-Markov Factor Analysis as a spatiotemporal model for electrocorticography.A. Omigbodun, W. Doyle, O. Devinsky, D. Friedman, T. Thesen, and V. Gilja. EMBC, page 1632-1635. IEEE, (2016)Localized Motion Artifact Reduction on Brain MRI Using Deep Learning with Effective Data Augmentation Techniques.Y. Zhao, J. Ossowski, X. Wang, S. Li, O. Devinsky, S. Martin, and H. Pardoe. IJCNN, page 1-9. IEEE, (2021)Multi-task Learning with Weak Class Labels: Leveraging iEEG to Detect Cortical Lesions in Cryptogenic Epilepsy.B. Ahmed, T. Thesen, K. Blackmon, R. Kuzniecky, O. Devinsky, J. Dy, and C. Brodley. MLHC, volume 56 of JMLR Workshop and Conference Proceedings, page 115-133. JMLR.org, (2016)

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The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models.

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The Temporal Structure of Language Processing in the Human Brain Corresponds to The Layered Hierarchy of Deep Language Models.