Publication

Found 171 results
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2023
Caldarelli, E., Chatalic, A., Colom´e, A. `a, Rosasco, L. & Torras, C. Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees. 7th Conference on Robot Learning (CoRL 2023 (2023). at <https://proceedings.mlr.press/v229/caldarelli23a/caldarelli23a.pdf>
Chen, Z. Sage & Wilson, M. A. How our understanding of memory replay evolves. Journal of Neurophysiology 129, 552 - 580 (2023).
Singhal, U. et al. How to Guess a Gradient. arXiv (2023). at <https://arxiv.org/abs/2312.04709>
Singhal, U. et al. How to Guess a Gradient. arXiv (2023). at <https://arxiv.org/abs/2312.04709>
Allen, K. R. et al. Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experienceAbstract. Psychonomic Bulletin & Review (2023). doi:10.3758/s13423-023-02400-4
Schiatti, L. et al. Modeling Visual Impairments with Artificial Neural Networks: a Review. International Conference on Computer Vision 2023 (2023). at <https://openaccess.thecvf.com/content/ICCV2023W/ACVR/html/Schiatti_Modeling_Visual_Impairments_with_Artificial_Neural_Networks_a_Review_ICCVW_2023_paper.html>
Yildirim, I., Siegel, M. H., Soltani, A. A., Chaudhuri, S. Ray & Tenenbaum, J. B. Perception of 3D shape integrates intuitive physics and analysis-by-synthesis. Nature Human Behaviour (2023). doi:10.1038/s41562-023-01759-7
Gershman, S. J. & Cikara, M. Structure learning principles of stereotype change. Psychonomic Bulletin & Review 30, 1273 - 1293 (2023).
Duan, A. et al. A structured prediction approach for robot imitation learning. The International Journal of Robotics Research 43, 113 - 133 (2023).
Han, Y., Poggio, T. & Cheung, B. System Identification of Neural Systems: If We Got It Right, Would We Know?. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 12430-12444 (2023).PDF icon han23d.pdf (797.48 KB)
Vélez, N., Chen, A. M., Burke, T., Cushman, F. A. & Gershman, S. J. Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).
Vélez, N., Chen, A. M., Burke, T., Cushman, F. A. & Gershman, S. J. Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).
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>
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>
2024
Madan, S. et al. Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex. NeurIPS 2024 (2024).
Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).PDF icon CBMM-Memo-150.pdf (4.03 MB)
Ceola, F., Rosasco, L., Natale, L. & Ceola, F. RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4. IEEE Robotics and Automation Letters 9, 3045 - 3052 (2024).
Ceola, F., Rosasco, L., Natale, L. & Ceola, F. RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4. IEEE Robotics and Automation Letters 9, 3045 - 3052 (2024).
Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).PDF icon CBMM-Memo-152.pdf (1.84 MB)
Mendoza-Halliday, D. et al. A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract. Nature Neuroscience 27, 547 - 560 (2024).
2025
Tiwary, K. et al. What if Eye..? Computationally Recreating Vision Evolution. arXiv (2025). at <https://arxiv.org/abs/2501.15001>PDF icon 2501.15001v1.pdf (5.2 MB)

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