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Martin Schrimpf
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2020 – today
- 2024
- [i12]Badr AlKhamissi, Greta Tuckute, Antoine Bosselut, Martin Schrimpf:
Brain-Like Language Processing via a Shallow Untrained Multihead Attention Network. CoRR abs/2406.15109 (2024) - [i11]Neil Rathi, Johannes Mehrer, Badr AlKhamissi, Taha Binhuraib, Nicholas M. Blauch, Martin Schrimpf:
TopoLM: brain-like spatio-functional organization in a topographic language model. CoRR abs/2410.11516 (2024) - [i10]Badr AlKhamissi, Yingtian Tang, Abdülkadir Gökce, Johannes Mehrer, Martin Schrimpf:
Dreaming Out Loud: A Self-Synthesis Approach For Training Vision-Language Models With Developmentally Plausible Data. CoRR abs/2411.00828 (2024) - 2023
- [c8]Lucia Schiatti, Monica Gori, Martin Schrimpf, Giulia Cappagli, Federica Morelli, Sabrina Signorini, Boris Katz, Andrei Barbu:
Modeling Visual Impairments with Artificial Neural Networks: a Review. ICCV (Workshops) 2023: 1979-1991 - [c7]Joel Dapello, Kohitij Kar, Martin Schrimpf, Robert Baldwin Geary, Michael Ferguson, David Daniel Cox, James J. DiCarlo:
Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness. ICLR 2023 - [i9]Khai Loong Aw, Syrielle Montariol, Badr AlKhamissi, Martin Schrimpf, Antoine Bosselut:
Instruction-tuning Aligns LLMs to the Human Brain. CoRR abs/2312.00575 (2023) - 2022
- [c6]Franziska Geiger, Martin Schrimpf, Tiago Marques, James J. DiCarlo:
Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. ICLR 2022 - 2021
- [j1]Chengxu Zhuang
, Siming Yan
Unsupervised neural network models of the ventral visual stream. Proc. Natl. Acad. Sci. USA 118(3): e2014196118 (2021) - [c5]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf, Kasper Vinken, Gabriel Kreiman:
Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021: 6921-6929 - [c4]Chuang Gan, Jeremy Schwartz, Seth Alter, Damian Mrowca, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel Bear, Dan Gutfreund, David D. Cox, Antonio Torralba, James J. DiCarlo, Josh Tenenbaum, Josh H. McDermott, Dan Yamins:
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c3]Joel Dapello, Tiago Marques, Martin Schrimpf, Franziska Geiger, David D. Cox, James J. DiCarlo:
Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. NeurIPS 2020 - [i8]Chuang Gan, Jeremy Schwartz, Seth Alter, Martin Schrimpf, James Traer, Julian De Freitas, Jonas Kubilius, Abhishek Bhandwaldar, Nick Haber, Megumi Sano, Kuno Kim, Elias Wang, Damian Mrowca, Michael Lingelbach, Aidan Curtis, Kevin T. Feigelis, Daniel M. Bear, Dan Gutfreund, David D. Cox, James J. DiCarlo, Josh H. McDermott, Joshua B. Tenenbaum, Daniel L. K. Yamins:
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. CoRR abs/2007.04954 (2020)
2010 – 2019
- 2019
- [c2]Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo:
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. NeurIPS 2019: 12785-12796 - [i7]Pouya Bashivan, Martin Schrimpf
Continual Learning with Self-Organizing Maps. CoRR abs/1904.09330 (2019) - [i6]Jonas Kubilius, Martin Schrimpf, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Aran Nayebi, Daniel Bear, Daniel L. K. Yamins, James J. DiCarlo:
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. CoRR abs/1909.06161 (2019) - [i5]Stephen Casper, Xavier Boix, Vanessa D'Amario, Ling Guo, Martin Schrimpf
Removable and/or Repeated Units Emerge in Overparametrized Deep Neural Networks. CoRR abs/1912.04783 (2019) - 2018
- [c1]Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher:
A Flexible Approach to Automated RNN Architecture Generation. ICLR (Workshop) 2018 - 2017
- [i4]Nicholas Cheney, Martin Schrimpf
On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. CoRR abs/1703.08245 (2017) - [i3]Hanlin Tang, Bill Lotter, Martin Schrimpf, Ana Paredes, Josue Ortega Caro, Walter Hardesty, David D. Cox, Gabriel Kreiman:
Recurrent computations for visual pattern completion. CoRR abs/1706.02240 (2017) - [i2]Martin Schrimpf, Stephen Merity, James Bradbury, Richard Socher:
A Flexible Approach to Automated RNN Architecture Generation. CoRR abs/1712.07316 (2017) - 2016
- [i1]Martin Schrimpf:
Should I use TensorFlow. CoRR abs/1611.08903 (2016)
Coauthor Index
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