Martin N. Hebart

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 16

Top Topics

Syntactic Features

Computational Model

Object Representations

Top Venues

Frontiers Neuroinformatics

Nat. Mac. Intell.

Publications

Katja Seeliger, Martin N. Hebart
What comparing deep neural networks can teach us about human vision. Nat. Mac. Intell. 6 (2) (2024)
Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart
Dimensions underlying the representational alignment of deep neural networks with humans. CoRR (2024)
Martin N. Hebart
Revealing interpretable object representations from human visual cortex and artificial neural networks. BCI (2023)
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 (2023)
Philipp Kaniuth, Martin N. Hebart
Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. NeuroImage 257 (2022)
Hannes Hansen, Martin N. Hebart
Semantic features of object concepts generated with GPT-3. CogSci (2022)
Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira
VICE: Variational Interpretable Concept Embeddings. NeurIPS (2022)
Hannes Hansen, Martin N. Hebart
Semantic features of object concepts generated with GPT-3. CoRR (2022)
Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira
VICE: Variational Interpretable Concept Embeddings. CoRR (2022)
Lukas Muttenthaler, Martin N. Hebart
THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks. Frontiers Neuroinformatics 15 (2021)
Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart
Revealing interpretable object representations from human behavior. CoRR (2019)
Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart
Revealing interpretable object representations from human behavior. ICLR (Poster) (2019)
Martin N. Hebart, Chris I. Baker
Deconstructing multivariate decoding for the study of brain function. NeuroImage 180 (Part) (2018)
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) (2018)
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 (2018)
Johannes Höhne, Daniel Bartz, Martin N. Hebart, Klaus-Robert Müller, Benjamin Blankertz
Analyzing neuroimaging data with subclasses: A shrinkage approach. NeuroImage 124 (2016)
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 (2015)
Judith Peth, Tobias Sommer, Martin N. Hebart, Gerhard Vossel, Christian Büchel, Matthias Gamer
Memory detection using fMRI - Does the encoding context matter? NeuroImage 113 (2015)
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 (2014)
Martin N. Hebart, Tobias H. Donner, John-Dylan Haynes
Human visual and parietal cortex encode visual choices independent of motor plans. NeuroImage 63 (3) (2012)