All Publications

2023

J. Feather, Leclerc, G., Mądry, A., and McDermott, J. H., “Model metamers reveal divergent invariances between biological and artificial neural networks”, Nature Neuroscience, 2023.
CBMM Funded
G. Gaziv, Lee, M. J., and DiCarlo, J. J., “Robustified ANNs Reveal Wormholes Between Human Category Percepts”, arXiv, 2023.
CBMM Funded
T. Poggio and Magrini, M., Cervelli menti algoritmi. Sperling & Kupfer, 2023, p. 272.
CBMM Funded
L. Schiatti, Gori, M., Schrimpf, M., Cappagli, G., Morelli, F., Signorini, S., Katz, B., and Barbu, A., “Modeling Visual Impairments with Artificial Neural Networks: a Review”, in International Conference on Computer Vision 2023, Paris, 2023.
CBMM Funded
P. Singh, Li, Y., Sikarwar, A., Lei, S. Weixian, Gao, D., Talbot, M. B., Sun, Y., Shou, M. Zheng, Kreiman, G., and Zhang, M., “ Learning to Learn: How to Continuously Teach Humans and Machines ”, in International Conference on Computer Vision (ICCV), 2023, Paris, France, 2023.
CBMM Funded
C. Vega, Molinari, C., Rosasco, L., and Villa, S., “Fast iterative regularization by reusing dataAbstract”, Journal of Inverse and Ill-posed Problems, 2023.
CBMM Funded
CBMM Memo No.
143
Y. Gan and Poggio, T. A., “A Homogeneous Transformer Architecture”. 2023.PDF icon CBMM-Memo-143.pdf (1.07 MB)
CBMM Funded
E. J. Bigelow, McCoy, J. P., and Ullman, T. D., “Non-commitment in mental imagery”, Cognition, vol. 238, p. 105498, 2023.
CBMM Funded
P. A. Feliciano-Ramos, Galazo, M., Penagos, H., and Wilson, M., “Hippocampal memory reactivation during sleep is correlated with specific cortical states of the retrosplenial and prefrontal cortices”, Learning & Memory, vol. 30, no. 9, pp. 221 - 236, 2023.
CBMM Related
T. P. O'Connell, Bonnen, T., Friedman, Y., Tewari, A., Tenenbaum, J. B., Sitzmann, V., and Kanwisher, N., “Approaching human 3D shape perception with neurally mappable models”, arXiv, 2023.
CBMM Funded
T. Bricken, Schaeffer, R., Olshausen, B., and Kreiman, G., “Emergence of Sparse Representations from Noise”, ICML 2023. Honolulu, HI, 2023.
CBMM Funded
C. Consortium, Ferrante, O., Gorska-Klimowska, U., Henin, S., Hirschhorn, R., Khalaf, A., Lepauvre, A., Liu, L., Richter, D., Vidal, Y., Bonacchi, N., Brown, T., Sripad, P., Armendariz, M., Bendtz, K., Ghafari, T., Hetenyi, D., Jeschke, J., Kozma, C., Mazumder, D. R., Montenegro, S., Seedat, A., Sharafeldin, A., Yang, S., Baillet, S., Chalmers, D. J., Cichy, R. M., Fallon, F., Panagiotaropoulos, T. I., Blumenfeld, H., de Lange, F. P., Devore, S., Jensen, O., Kreiman, G., Luo, H., Boly, M., Dehaene, S., Koch, C., Tononi, G., Pitts, M., Mudrik, L., and Melloni, L., “An adversarial collaboration to critically evaluate theories of consciousness”, bioRxiv, 2023.
CBMM Related
K. Dobs, Yuan, J., Martinez, J., and Kanwisher, N., “Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition”, Proceedings of the National Academy of Sciences, vol. 120, no. 32, 2023.
CBMM Funded
Y. Han, Poggio, T. A., and 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, vol. 202. pp. 12430-12444, 2023.PDF icon han23d.pdf (797.48 KB)
CBMM Funded
S. Dae Houlihan, Kleiman-Weiner, M., Hewitt, L. B., Tenenbaum, J. B., and Saxe, R., “Emotion prediction as computation over a generative theory of mind”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 381, no. 2251, 2023.PDF icon houlihan2023computedappraisals.pdf (2.37 MB)
CBMM Funded
X. Puig, Shu, T., Tenenbaum, J. B., and Torralba, A., “NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants”, in 2023 IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023.
CBMM Related
CBMM Memo No.
142
Y. Xie, Li, Y., and Rangamani, A., “Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms”. 2023.PDF icon CBMM-Memo-142.pdf (1.64 MB)
CBMM Funded
Y. Xiang, Vélez, N., and Gershman, S. J., “Collaborative decision making is grounded in representations of other people’s competence and effort.”, Journal of Experimental Psychology: General, vol. 152, no. 6, pp. 1565 - 1579, 2023.
CBMM Funded
S. J. Gershman and Ullman, T. D., “Causal implicatures from correlational statements”, PLOS ONE, vol. 18, no. 5, p. e0286067, 2023.
CBMM Funded
N. Vélez, Chen, A. M., Burke, T., Cushman, F. A., and Gershman, S. J., “Teachers recruit mentalizing regions to represent learners’ beliefs”, Proceedings of the National Academy of Sciences, vol. 120, no. 22, 2023.
CBMM Funded
D. Lagomarsino-Oneto, Meanti, G., Pagliana, N., Verri, A., Mazzino, A., Rosasco, L., and Seminara, A., “Physics informed machine learning for wind speed prediction”, Energy, vol. 268, p. 126628, 2023.
CBMM Funded
T. Bricken, Davies, X., Singh, D., Krotov, D., and Kreiman, G., “Sparse distributed memory is a continual learner”, International Conference on Learning Representations. Kigali, Rwanda, Africa, 2023.PDF icon 6086_sparse_distributed_memory_is_a.pdf (13.3 MB)
CBMM Funded
S. J. Gershman and Cikara, M., “Structure learning principles of stereotype change”, Psychonomic Bulletin & Review, vol. 30, no. 4, pp. 1273 - 1293, 2023.
CBMM Funded
N. Kanwisher, Khosla, M., and Dobs, K., “Using artificial neural networks to ask ‘why’ questions of minds and brains”, Trends in Neurosciences, vol. 46, no. 3, pp. 240 - 254, 2023.
CBMM Related
H. L. Kosakowski, Norman‐Haignere, S., Mynick, A., Takahashi, A., Saxe, R., and Kanwisher, N., “Preliminary evidence for selective cortical responses to music in one‐month‐old infants”, Developmental Science, 2023.PDF icon Developmental Science - 2023 - Kosakowski - Preliminary evidence for selective cortical responses to music in one‐month‐old.pdf (2.6 MB)
CBMM Funded

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