Publication

Found 142 results
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2022
Zaslavsky, N., Garvin, K., Kemp, C., Tishby, N. & Regier, T. The evolution of color naming reflects pressure for efficiency: Evidence from the recent pastAbstract. Journal of Language Evolution (2022). doi:10.1093/jole/lzac001
Letizia, M. et al. Learning new physics efficiently with nonparametric methodsAbstract. The European Physical Journal C 82, (2022).
Letizia, M. et al. Learning new physics efficiently with nonparametric methodsAbstract. The European Physical Journal C 82, (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)
Banburski, A. & Rangamani, A. Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay. IEEE International Conference on Acoustics, Speech and Signal Processing (2022).
Norman-Haignere, S. V. et al. A neural population selective for song in human auditory cortex. Current Biology (2022). doi:10.1016/j.cub.2022.01.069PDF icon mmc2.pdf (9.52 MB)
Zheng, J. et al. Neurons detect cognitive boundaries to structure episodic memories in humans. Nature Neuroscience 25, 358 - 368 (2022).
Montagna, F., Noceti, N., Rosasco, L., Zhang, K. & Locatello, F. Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>
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)
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928
Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).PDF icon CBMM-Memo-132.pdf (1.15 MB)
Rangamani, A. & Xie, Y. Understanding the Role of Recurrent Connections in Assembly Calculus. (2022).PDF icon CBMM-Memo-137.pdf (1.49 MB)
Kamps, F. S., Richardson, H., N. Murty, A. Ratan, Kanwisher, N. & Saxe, R. Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years. Human Brain Mapping (2022). doi:10.1002/hbm.25815
2023
Melloni, L. et al. An adversarial collaboration protocol for testing contrasting predictions of global neuronal workspace and integrated information theory. PLOS ONE 18, e0268577 (2023).PDF icon journal.pone_.0268577.pdf (1.99 MB)
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
Zador, A. et al. Catalyzing next-generation Artificial Intelligence through NeuroAIAbstract. Nature Communications 14, (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)
Zhang, Y. et al. Decoding of human identity by computer vision and neuronal vision. Scientific Reports 13, (2023).PDF icon s41598-022-26946-w.pdf (1.88 MB)
Zhang, Y. et al. Decoding of human identity by computer vision and neuronal visionAbstract. Scientific Reports 13, (2023).
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)
Meanti*, G. et al. Estimating Koopman operators with sketching to provably learn large scale dynamical systems. 37th Conference on Neural Information Processing Systems (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/f3d1e34a15c0af0954ae36a7f811c754-Paper-Conference.pdf>
Vega, C., Molinari, C., Rosasco, L. & Villa, S. Fast iterative regularization by reusing dataAbstract. Journal of Inverse and Ill-posed Problems (2023). doi:10.1515/jiip-2023-0009
Rangamani, A., Lindegaard, M., Galanti, T. & Poggio, T. Feature learning in deep classifiers through Intermediate Neural Collapse. (2023).PDF icon Feature_Learning_memo.pdf (2.16 MB)
Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability. Analysis and Applications 21, 193 - 215 (2023).
Rangamani, A., Rosasco, L. & Poggio, T. For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability. Analysis and Applications 21, 193 - 215 (2023).

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