Martin N. Hebart

According to our database, Martin N. Hebart authored at least 17 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

What comparing deep neural networks can teach us about human vision.

Katja Seeliger, Martin N. Hebart

Nat. Mac. Intell., 2024

Dimensions underlying the representational alignment of deep neural networks with humans.

Florian P. Mahner, Lukas Muttenthaler, Umut Güçlü, Martin N. Hebart

CoRR, 2024

2023

Getting aligned on representational alignment.

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

CoRR, 2023

Revealing interpretable object representations from human visual cortex and artificial neural networks.

Martin N. Hebart

Proceedings of the 11th International Winter Conference on Brain-Computer Interface, 2023

2022

Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior.

Philipp Kaniuth, Martin N. Hebart

NeuroImage, 2022

VICE: Variational Interpretable Concept Embeddings.

Lukas Muttenthaler, Charles Y. Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N. Hebart, Francisco Pereira

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Semantic features of object concepts generated with GPT-3.

Hannes Hansen, Martin N. Hebart

Proceedings of the 44th Annual Meeting of the Cognitive Science Society, 2022

2021

THINGSvision: A Python Toolbox for Streamlining the Extraction of Activations From Deep Neural Networks.

Lukas Muttenthaler, Martin N. Hebart

Frontiers Neuroinformatics, 2021

2019

Revealing interpretable object representations from human behavior.

Charles Y. Zheng, Francisco Pereira, Chris I. Baker, Martin N. Hebart

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Deconstructing multivariate decoding for the study of brain function.

Martin N. Hebart, Chris I. Baker

NeuroImage, 2018

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods.

Kai Görgen, Martin N. Hebart, Carsten Allefeld, John-Dylan Haynes

NeuroImage, 2018

The temporal evolution of conceptual object representations revealed through models of behavior, semantics and deep neural networks.

B. B. Bankson, Martin N. Hebart, Iris I. A. Groen, Chris I. Baker

NeuroImage, 2018

2016

Analyzing neuroimaging data with subclasses: A shrinkage approach.

Johannes Höhne, Daniel Bartz, Martin N. Hebart, Klaus-Robert Müller, Benjamin Blankertz

NeuroImage, 2016

2015

Memory detection using fMRI - Does the encoding context matter?

Judith Peth, Tobias Sommer, Martin N. Hebart, Gerhard Vossel, Christian Büchel, Matthias Gamer

NeuroImage, 2015

Parietal and early visual cortices encode working memory content across mental transformations.

Thomas B. Christophel, Radoslaw Martin Cichy, Martin N. Hebart, John-Dylan Haynes

NeuroImage, 2015

2014

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data.

Martin N. Hebart, Kai Görgen, John-Dylan Haynes

Frontiers Neuroinformatics, 2014

2012

Human visual and parietal cortex encode visual choices independent of motor plans.

Martin N. Hebart, Tobias H. Donner, John-Dylan Haynes

NeuroImage, 2012