Export 16 results:
Filters: Author is DiCarlo, James J  [Clear All Filters]
Guo C, Lee MJ, Leclerc G, et al. Adversarially trained neural representations may already be as robust as corresponding biological neural representations. arXiv. 2022. doi: (1.99 MB)
Geiger F, Schrimpf M, Marques T, DiCarlo JJ. Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream. In: International Conference on Learning Representations 2022 Spotlight. International Conference on Learning Representations 2022 Spotlight.; 2022. doi:10.1101/2020.06.08.140111. (1.45 MB)
Dapello J, Marques T, Schrimpf M, Geiger F, Cox DD, DiCarlo JJ. Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Neural Information Processing Systems (NeurIPS; spotlight). 2020. doi:10.1101/2020.06.16.154542. (2.48 MB)
Lee MJ, DiCarlo JJ. Comparing novel object learning in humans, models, and monkeys. Journal of Vision. 2019;19(10):114b. doi:10.1167/19.10.114b.
Schrimpf M, Kubilius J, Hong H, et al. Using Brain-Score to Evaluate and Build Neural Networks for Brain-Like Object Recognition. In: Computational and Systems Neuroscience (COSYNE). Computational and Systems Neuroscience (COSYNE). Denver, CO; 2019.
Pinto N, Cox DD, DiCarlo JJ. Why is Real-World Visual Object Recognition Hard?. Friston KJ. PLoS Computational Biology. 2008;4:e27. doi:10.1371/journal.pcbi.0040027. (1.93 MB)
Kourtzi Z, DiCarlo JJ. Learning and neural plasticity in visual object recognition. Current Opinion in Neurobiology. 2006;16(2):152 - 158. doi:10.1016/j.conb.2006.03.012. (181.23 KB)
Cox DD, Meier P, Oertelt N, DiCarlo JJ. 'Breaking' position-invariant object recognition. Nature Neuroscience. 2005;8(9):1145 - 1147. doi:10.1038/nn1519. (175.59 KB) (49.96 KB) (87.63 KB)
DiCarlo JJ, Johnson KO. Receptive field structure in cortical area 3b of the alert monkey. Behavioural Brain Research. 2002;135(1-2):167 - 178. doi:10.1016/S0166-4328(02)00162-6. (382.63 KB)