Martin Schrimpf
Affiliations: | 2011-2014 | Informatik | TU Munich, München, Bayern, Germany |
| 2014-2017 | TU Munich, München, Bayern, Germany | ||
| 2014-2017 | LMU Munich, München, Bayern, Germany | ||
| 2014-2017 | University of Augsburg | ||
| 2015-2015 | Siemens AG | ||
| 2015-2016 | Labs | Oracle | |
| 2016-2016 | Medical School | Harvard University, Cambridge, MA, United States | |
| 2017- | Brain and Cognitive Sciences | Massachusetts Institute of Technology, Cambridge, MA, United States | |
| 2017-2017 | Einstein AI Research | Salesforce |
Area:
computational neuroscience, vision, deep learning, artificial neural networksWebsite:
http://mschrimpf.com/Google:
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Parents
Sign in to add mentor| Gabriel Kreiman | research assistant | 2016-2016 | Children's Hospital and Harvard Medical School |
| James J. DiCarlo | grad student | 2017- | MIT |
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Publications
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| DiCarlo JJ, Yamins DLK, Ferguson ME, et al. (2024) Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision - CORRIGENDUM. The Behavioral and Brain Sciences. 47: e66 |
| Tuckute G, Sathe A, Srikant S, et al. (2024) Driving and suppressing the human language network using large language models. Nature Human Behaviour |
| DiCarlo JJ, Yamins DLK, Ferguson ME, et al. (2023) Let's move forward: Image-computable models and a common model evaluation scheme are prerequisites for a scientific understanding of human vision. The Behavioral and Brain Sciences. 46: e390 |
| Tuckute G, Sathe A, Srikant S, et al. (2023) Driving and suppressing the human language network using large language models. Biorxiv : the Preprint Server For Biology |
| Schrimpf M, Blank IA, Tuckute G, et al. (2021) The neural architecture of language: Integrative modeling converges on predictive processing. Proceedings of the National Academy of Sciences of the United States of America. 118 |
| Zhuang C, Yan S, Nayebi A, et al. (2021) Unsupervised neural network models of the ventral visual stream. Proceedings of the National Academy of Sciences of the United States of America. 118 |
| Schrimpf M, Kubilius J, Lee MJ, et al. (2020) Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence. Neuron |
| Tang H, Schrimpf M, Lotter W, et al. (2018) Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences of the United States of America |