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ORCIDMartin N. Hebart
Person information
- affiliation: National Institute of Mental Health, Bethesda, MD, USA
- affiliation: University Medical Center Hamburg-Eppendorf, Department of Systems Neuroscience, Germany
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2020 – today
- 2023
[c4]Martin N. Hebart
:
Revealing interpretable object representations from human visual cortex and artificial neural networks. BCI 2023: 1-3- [i4]Ilia Sucholutsky, Lukas Muttenthaler, Adrian Weller, Andi Peng, Andreea Bobu, Been Kim, Bradley C. Love, Erin Grant, Jascha Achterberg, Joshua B. Tenenbaum, Katherine M. Collins, Katherine L. Hermann, Kerem Oktar, Klaus Greff, Martin N. Hebart, Nori Jacoby, Qiuyi Zhang, Raja Marjieh, Robert Geirhos, Sherol Chen, Simon Kornblith, Sunayana Rane, Talia Konkle, Thomas P. O'Connell, Thomas Unterthiner, Andrew K. Lampinen, Klaus-Robert Müller, Mariya Toneva, Thomas L. Griffiths:
Getting aligned on representational alignment. CoRR abs/2310.13018 (2023) - 2022
- [j10]Philipp Kaniuth, Martin N. Hebart:
Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. NeuroImage 257: 119294 (2022) - [c3]Hannes Hansen, Martin N. Hebart:
Semantic features of object concepts generated with GPT-3. CogSci 2022 - [c2]Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira:
VICE: Variational Interpretable Concept Embeddings. NeurIPS 2022 - [i3]Hannes Hansen, Martin N. Hebart:
Semantic features of object concepts generated with GPT-3. CoRR abs/2202.03753 (2022) - [i2]Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira:
VICE: Variational Interpretable Concept Embeddings. CoRR abs/2205.00756 (2022) - 2021
- [j9]Lukas Muttenthaler, Martin N. Hebart:
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks. Frontiers Neuroinformatics 15: 679838 (2021)
2010 – 2019
- 2019
- [c1]Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart:
Revealing interpretable object representations from human behavior. ICLR (Poster) 2019 - [i1]Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart:
Revealing interpretable object representations from human behavior. CoRR abs/1901.02915 (2019) - 2018
- [j8]B. B. Bankson, Martin N. Hebart, Iris I. A. Groen, Chris I. Baker:
The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks. NeuroImage 178: 172-182 (2018) - [j7]Martin N. Hebart, Chris I. Baker:
Deconstructing multivariate decoding for the study of brain function. NeuroImage 180(Part): 4-18 (2018) - [j6]Kai Görgen, Martin N. Hebart, Carsten Allefeld, John-Dylan Haynes:
The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods. NeuroImage 180(Part): 19-30 (2018) - 2016
- [j5]Johannes Höhne, Daniel Bartz, Martin N. Hebart, Klaus-Robert Müller, Benjamin Blankertz:
Analyzing neuroimaging data with subclasses: A shrinkage approach. NeuroImage 124: 740-751 (2016) - 2015
- [j4]Thomas B. Christophel, Radoslaw Martin Cichy, Martin N. Hebart, John-Dylan Haynes:
Parietal and early visual cortices encode working memory content across mental transformations. NeuroImage 106: 198-206 (2015) - [j3]Judith Peth, Tobias Sommer, Martin N. Hebart, Gerhard Vossel, Christian Büchel, Matthias Gamer:
Memory detection using fMRI - Does the encoding context matter? NeuroImage 113: 164-174 (2015) - 2014
- [j2]Martin N. Hebart, Kai Görgen, John-Dylan Haynes:
The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data. Frontiers Neuroinformatics 8: 88 (2014) - 2012
- [j1]Martin N. Hebart, Tobias H. Donner, John-Dylan Haynes:
Human visual and parietal cortex encode visual choices independent of motor plans. NeuroImage 63(3): 1393-1403 (2012)
Coauthor Index
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