Publication

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Filters: Author is Gabriel Kreiman  [Clear All Filters]
2025
Bono, S. et al. The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function. (2025). at <https://arxiv.org/abs/2401.15856>
2024
Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).
2023
Consortium, C. et al. An adversarial collaboration to critically evaluate theories of consciousness. bioRxiv (2023). doi:https://doi.org/10.1101/2023.06.23.546249
Wang, C. et al. BrainBERT: Self-supervised representation learning for Intracranial Electrodes. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=xmcYx_reUn6>PDF icon 985_brainbert_self_supervised_repr.pdf (9.71 MB)
Bricken, T., Schaeffer, R., Olshausen, B. & Kreiman, G. Emergence of Sparse Representations from Noise. ICML 2023 (2023). at <https://openreview.net/pdf?id=cxYaBAXVKg>
Srinivasan, R. Francesco et al. Forward learning with top-down feedback: empirical and analytical characterization. arXiv (2023). at <https://arxiv.org/abs/2302.05440>
Singh, P. et al. Learning to Learn: How to Continuously Teach Humans and Machines . International Conference on Computer Vision (ICCV), 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023/html/Singh_Learning_to_Learn_How_to_Continuously_Teach_Humans_and_Machines_ICCV_2023_paper.html>
Xiao, W., Sharma, S., Kreiman, G. & Livingstone, M. S. Out of sight, out of mind: Responses in primate ventral visual cortex track individual fixations during natural vision. bioRxiv (2023). doi:10.1101/2023.02.08.527666
Bricken, T., Davies, X., Singh, D., Krotov, D. & Kreiman, G. Sparse distributed memory is a continual learner. International Conference on Learning Representations (2023). at <https://openreview.net/forum?id=JknGeelZJpHP>PDF icon 6086_sparse_distributed_memory_is_a.pdf (13.3 MB)
Subramaniam, V. et al. Using Multimodal DNNs to Study Vision-Language Integration in the Brain. ICLR 2023 (2023). at <https://openreview.net/pdf?id=OQQ1p0pFP4>
2022
Armendariz, M., Xiao, W., Vinken, K. & Kreiman, G. Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology 1 - 3 (2022). doi:10.1080/02643294.2022.2041588
Sikarwar, A. & Kreiman, G. On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering. arXiv (2022). doi:10.48550/arXiv.2201.03965PDF icon On_the_Efficacy_of_Co-Attention_Transformer_Layers.pdf (35.54 MB)
Dellaferrera, G. & Kreiman, G. Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass. Proceedings of the 39th International Conference on Machine Learning, PMLR 162, 4937-4955 (2022).PDF icon dellaferrera22a.pdf (909.91 KB)
Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S. & Kreiman, G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).
Zheng, J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience 25, 358 - 368 (2022).
Casper, S., Nadeau, M. & Kreiman, G. One thing to fool them all: generating interpretable, universal, and physically-realizable adversarial features. arXiv (2022). doi:10.48550/arXiv.2110.03605PDF icon 2110.03605.pdf (6.7 MB)
Casper, S., Nadeau, M., Hadfield-Menell, D. & Kreiman, G. Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS (2022). at <https://openreview.net/forum?id=lQ--doSB2o>PDF icon 8789_robust_feature_level_adversari.pdf (3.79 MB)
Shaham, N., Chandra, J., Kreiman, G. & Sompolinsky, H. Stochastic consolidation of lifelong memoryAbstract. Scientific Reports 12, (2022).PDF icon s41598-022-16407-9.pdf (2.54 MB)
Xiao, Y. et al. Task-specific neural processes underlying conflict resolution during cognitive control. BioRxiv (2022). doi:10.1101/2022.01.16.476535 PDF icon 2022.01.16.476535v1.full_.pdf (22.96 MB)
2021
Zhang, M. & Kreiman, G. Beauty is in the eye of the machine. Nature Human Behaviour 5, 675 - 676 (2021).
Kreiman, G. Biological and Computer Vision. (Cambridge University Press, 2021). doi:10.1017/9781108649995
Zheng, J. et al. Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. bioRxiv (2021).
Casper, S. et al. Frivolous Units: Wider Networks Are Not Really That Wide. AAAI 2021 (2021). at <https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html>PDF icon 1912.04783.pdf (6.69 MB)
Zhang, M., Badkundri, R., Talbot, M. B., Zawar, R. & Kreiman, G. Hypothesis-driven Online Video Stream Learning with Augmented Memory. arXiv (2021). doi:10.48550/arXiv.2104.02206PDF icon 2104.02206.pdf (2.25 MB)
Weisholtz, D. S. et al. Localized task-invariant emotional valence encoding revealed by intracranial recordingsAbstract. Social Cognitive and Affective Neuroscience (2021). doi:10.1093/scan/nsab134

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