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In this paper, we define and apply representational stability analysis (ReStA), an intuitive way of analyzing neural language models.
Event-related fMRI and the hemodynamic response
Randy L Buckner. 1998 · 1998
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Content and cluster analysis: assessing representational similarity in neural systems
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Representational similarity analysis-connecting the branches of systems neuroscience
Nikolaus Kriegeskorte, Marieke Mur, and Peter A Bandettini. 2008 · 2008
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Predicting human brain activity associated with the meanings of nouns
Tom M Mitchell, Svetlana V Shinkareva, Andrew Carlson, Kai-Min Chang, Vicente L Malave, Robert A Mason, and Marcel Adam Just. 2008 · 2008
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Simple composition: A magnetoencephalography investigation into the comprehension of minimal linguistic phrases
Douglas K Bemis and Liina Pylkkänen. 2011 · 2011
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Selecting corpus-semantic models for neurolinguistic decoding
Brian Murphy, Partha Talukdar, and Tom Mitchell. 2012 · 2012
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Interpretable semantic vectors from a joint model of brain-and text-based meaning
Alona Fyshe, Partha P Talukdar, Brian Murphy, and Tom M Mitchell. 2014 · 2014
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Restrictive vs. non-restrictive composition: a magnetoencephalography study
Timothy Leffel, Miriam Lauter, Masha Westerlund, and Liina Pylkkänen. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
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Aligning context-based statistical models of language with brain activity during reading
Leila Wehbe, Ashish Vaswani, Kevin Knight, and Tom M. Mitchell. 2014b · 2014
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The role of the left anterior temporal lobe in semantic composition vs. semantic memory
Masha Westerlund and Liina Pylkkänen. 2014 · 2014
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Joachim Bingel, Maria Barrett, and Anders Søgaard. 2016 · 2016
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Natural speech reveals the semantic maps that tile human cerebral cortex
Alexander G Huth, Wendy A de Heer, Thomas L Griffiths, Frédéric E Theunissen, and Jack L Gallant. 2016 · 2016
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Rafal Jozefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu. 2016 · 2016
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Peng Qian, Xipeng Qiu, and Xuanjing Huang. 2016 · 2016
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Exploring semantic representation in brain activity using word embeddings
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Dhanush Dharmaretnam and Alona Fyshe. 2018 · 2018
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Incorporating context into language encoding models for fMRI
Shailee Jain and Alexander G. Huth. 2018 · 2018
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Sharp nearby, fuzzy far away: How neural language models use context
Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. 2018 · 2018
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Toward a universal decoder of linguistic meaning from brain activation
Francisco Pereira, Bin Lou, Brianna Pritchett, Samuel Ritter, Samuel J Gershman, Nancy Kanwisher, Matthew Botvinick, and Evelina Fedorenko. 2018 · 2018
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Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Yu-Ping Ruan, Zhen-Hua Ling, and Yu Hu. 2016 · 2016
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Evaluating word embeddings with fMRI and eye-tracking
Anders Søgaard. 2016 · 2016
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Larger-context language modelling with recurrent neural network
Tian Wang and Kyunghyun Cho. 2016 · 2016
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Speaking, seeing, understanding: Correlating semantic models with conceptual representation in the brain
Luana Bulat, Stephen Clark, and Ekaterina Shutova. 2017 · 2017
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Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity
Samira Abnar, Rasyan Ahmed, Max Mijnheer, and Willem Zuidema. 2018 · 2018
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Gromov-Wasserstein alignment of word embedding spaces
David Alvarez-Melis and Tommi Jaakkola. 2018 · 2018
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Universal sentence encoder for english
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil. 2018 · 2018
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Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, and John Hopcroft. 2018 · 2018
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Robust evaluation of language-brain encoding experiments
Lisa Beinborn, Samira Abnar, and Rochelle Choenni. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
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Brainbench: A brain-image test suite for distributional semantic models
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