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Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses

PloS one, 2014-11, Vol.9 (11), p.e112575-e112575

2014

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Stacked regressions and structured variance partitioning for interpretable brain maps

NeuroImage (Orlando, Fla.), 2024-09, Vol.298, p.120772, Article 120772

2024

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Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling

Frontiers in computational neuroscience, 2021-11, Vol.15, p.737324-737324

2021

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Brainprints: identifying individuals from magnetoencephalograms

Communications biology, 2022-08, Vol.5 (1), p.852-852, Article 852

2022

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Higher visual areas act like domain-general filters with strong selectivity and functional specialization

Journal of vision (Charlottesville, Va.), 2024-09, Vol.24 (10), p.1474

2024

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Selectivity for food in human ventral visual cortex

Communications biology, 2023-02, Vol.6 (1), p.175-175, Article 175

2023

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Using texture synthesis to identify the features supporting coarse and fine object categorization

Journal of vision (Charlottesville, Va.), 2024-09, Vol.24 (10), p.1242

2024

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A texture statistics encoding model reveals sensitivity to mid-level features across human visual cortex

Journal of vision (Charlottesville, Va.), 2023-08, Vol.23 (9), p.5520

2023

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Interpretable mid-level encoding models of human visual cortex reveal associations between feature and semantic tuning for natural scene images

Journal of vision (Charlottesville, Va.), 2022-12, Vol.22 (14), p.4118

2022

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Leveraging Vision and Language Generative Models to Understand the Visual Cortex

Journal of vision (Charlottesville, Va.), 2024-09, Vol.24 (10), p.1333

2024

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Combining computational controls with natural text reveals aspects of meaning composition

Nature Computational Science, 2022-11, Vol.2 (11), p.745-757

2022

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A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex

The Journal of neuroscience, 2023-05, Vol.43 (22), p.4144-4161

2023

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The lexical semantics of adjective–noun phrases in the human brain

Human brain mapping, 2019-10, Vol.40 (15), p.4457-4469

2019

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Incremental Language Comprehension Difficulty Predicts Activity in the Language Network but Not the Multiple Demand Network

Cerebral cortex (New York, N.Y. 1991), 2021-07, Vol.31 (9), p.4006-4023

2021

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Machine Learning and Interpretation in Neuroimaging: 4th International Workshop, MLINI 2014, Held at NIPS 2014, Montreal, QC, Canada, December 13, 2014, Revised Selected Papers

1st Edition 2016, 2016

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Tracking neural coding of perceptual and semantic features of concrete nouns

NeuroImage (Orlando, Fla.), 2012-08, Vol.62 (1), p.451-463

2012

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Neural Taskonomy: Explaining high-level visual processing of natural scenes using task-derived representations

Journal of vision (Charlottesville, Va.), 2021-09, Vol.21 (9), p.2687

2021

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Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features

Journal of vision (Charlottesville, Va.), 2023-04, Vol.23 (4), p.8-8

2023

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Semantic Representations during Language Comprehension Are Affected by Context

The Journal of neuroscience, 2023-04, Vol.43 (17), p.3144-3158

2023

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Computational Language Modeling and the Promise of In Silico Experimentation

Neurobiology of language, 2024-04, Vol.5 (1), p.80-106

2024

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Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset

Nature machine intelligence, 2023-12, Vol.5 (12), p.1415-1426

2023

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A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models

Neurons, behavior, data analysis, and theory, 2023-01, Vol.6 (1)

2023

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REGULARIZED BRAIN READING WITH SHRINKAGE AND SMOOTHING

The annals of applied statistics, 2015-12, Vol.9 (4), p.1997-2022

2015

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A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG

IEEE transactions on medical robotics and bionics, 2021-02, Vol.3 (1), p.44-52

2021

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Automated Speech Analysis for Psychosis Evaluation

Machine Learning and Interpretation in Neuroimaging, p.31-39

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Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses

Stacked regressions and structured variance partitioning for interpretable brain maps

Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling

Brainprints: identifying individuals from magnetoencephalograms

Higher visual areas act like domain-general filters with strong selectivity and functional specialization

Selectivity for food in human ventral visual cortex

Using texture synthesis to identify the features supporting coarse and fine object categorization

A texture statistics encoding model reveals sensitivity to mid-level features across human visual cortex

Interpretable mid-level encoding models of human visual cortex reveal associations between feature and semantic tuning for natural scene images

Leveraging Vision and Language Generative Models to Understand the Visual Cortex

Combining computational controls with natural text reveals aspects of meaning composition

A Texture Statistics Encoding Model Reveals Hierarchical Feature Selectivity across Human Visual Cortex

The lexical semantics of adjective–noun phrases in the human brain

Incremental Language Comprehension Difficulty Predicts Activity in the Language Network but Not the Multiple Demand Network

Machine Learning and Interpretation in Neuroimaging: 4th International Workshop, MLINI 2014, Held at NIPS 2014, Montreal, QC, Canada, December 13, 2014, Revised Selected Papers

Tracking neural coding of perceptual and semantic features of concrete nouns

Neural Taskonomy: Explaining high-level visual processing of natural scenes using task-derived representations

Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features

Semantic Representations during Language Comprehension Are Affected by Context

Computational Language Modeling and the Promise of In Silico Experimentation

Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset

A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models

REGULARIZED BRAIN READING WITH SHRINKAGE AND SMOOTHING

A Deep Learning Model for Automated Classification of Intraoperative Continuous EMG

Automated Speech Analysis for Psychosis Evaluation

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