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Found 53 results
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2025
Poggio, T. A. & Xu, M. On efficiently computable functions, deep networks and sparse compositionality. (2025).PDF icon Deep_sparse_networks_approximate_efficiently_computable_functions.pdf (223.15 KB)
2024
Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).
Mendoza-Halliday, D., Xu, H., Azevedo, F. A. C. & Desimone, R. Dissociable neuronal substrates of visual feature attention and working memory. Neuron 112, 850 - 863.e6 (2024).
Mendoza-Halliday, D. et al. A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).
2023
Xiang, Y., Landy, J., Cushman, F. A., Vélez, N. & Gershman, S. J. Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks. Cognition 241, 105609 (2023).
Xiang, Y., Vélez, N. & Gershman, S. J. Collaborative decision making is grounded in representations of other people’s competence and effort. Journal of Experimental Psychology: General 152, 1565 - 1579 (2023).
Xiao, Y. et al. Cross-task specificity and within-task invariance of cognitive control processes. Cell Reports 42, 111919 (2023).PDF icon PIIS2211124722018174.pdf (3.97 MB)
Xu, M., Rangamani, A., Liao, Q., Galanti, T. & Poggio, T. Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds. Research (2023). doi:10.34133/research.0024PDF icon research.0024.pdf (4.05 MB)
Xu, M. et al. The Janus effects of SGD vs GD: high noise and low rank. (2023).PDF icon Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)PDF icon Small updates...typos... (616.82 KB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf>PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
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
Xie, Y., Li, Y. & Rangamani, A. Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms. (2023).PDF icon CBMM-Memo-142.pdf (1.64 MB)
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
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).
Xu, C. et al. Lecture Notes in Computer ScienceComputer Vision – ECCV 2022Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models. 13697, 638 - 656 (Springer Nature Switzerland, 2022).
Zhang, M. et al. Look twice: A generalist computational model predicts return fixations across tasks and species. PLOS Computational Biology 18, e1010654 (2022).PDF icon journal.pcbi_.1010654.pdf (4.51 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)
Rangamani, A. & Xie, Y. Understanding the Role of Recurrent Connections in Assembly Calculus. (2022).PDF icon CBMM-Memo-137.pdf (1.49 MB)
2021
Xiang, Y., Graeber, T., Enke, B. & Gershman, S. J. Confidence and central tendency in perceptual judgment. Attention, Perception, & Psychophysics 83, 3024 - 3034 (2021).
Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).PDF icon v1.0 (4.61 MB)PDF icon v1.4corrections to generalization section (5.85 MB)PDF icon v1.7Small edits (22.65 MB)
Yang, C. et al. Evolutionary and biomedical insights from a marmoset diploid genome assembly. Nature (2021). doi:10.1038/s41586-021-03535-x
2020
Smith, K. A. et al. The fine structure of surprise in intuitive physics: when, why, and how much?. Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020 (Denison, S., Mack, M., Xu, Y. & Armstrong, B. C.) (2020). at <https://cogsci.mindmodeling.org/2020/papers/0761/index.html>
Poggio, T., Liao, Q. & Xu, M. Implicit dynamic regularization in deep networks. (2020).PDF icon v1.2 (2.29 MB)PDF icon v.59 Update on rank (2.43 MB)
Xiao, W. & Kreiman, G. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020).PDF icon gk7791.pdf (2.39 MB)

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