Publication

Found 912 results
Author [ Title(Asc)] Type Year
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Dillon, M. R., Huang, Y. & Spelke, E. S. Core foundations of abstract geometry. Proceedings of National Academy of Sciences of the United States of America 110, (2013).
Kleiman-Weiner, M., Ho, M. K., Austerweil, J. L., L, L. Michael & Tenenbaum, J. B. Coordinate to cooperate or compete: abstract goals and joint intentions in social interaction. Proceedings of the 38th Annual Conference of the Cognitive Science Society (2016).PDF icon kleiman2016coordinate.pdf (266.87 KB)
Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (Cosyne) 2018 (2018).
Mlynarski, W. & McDermott, J. H. Co-occurrence statistics of natural sound features predict perceptual grouping. Computational and Systems Neuroscience (COSYNE) (2018). at <http://www.cosyne.org/c/index.php?title=Cosyne_18>
Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. Nineteenth Conference on Computational Natural Language Learning (CoNLL), Beijing, China (2015).
Berzak, Y., Reichart, R. & Katz, B. Contrastive Analysis with Predictive Power: Typology Driven Estimation of Grammatical Error Distributions in ESL. (2016).PDF icon memo-50.pdf (493.74 KB)
Fisher, C. & Freiwald, W. A. Contrasting Specializations for Facial Motion within the Macaque Face-Processing System. Current Biology 25, (2015).PDF icon Facial Motion Selectivity in the Macaque Brain (1.43 MB)
Liu, S. & Spelke, E. S. Continuous representations of action efficiency in infancy. CEU Conference on Cognitive Development (BCCCD16) (2016).
Adler, A. & Wax, M. Constant Modulus Beamforming Via Low-Rank Approximation. 2018 IEEE Statistical Signal Processing Workshop (SSP) (2018). doi:10.1109/SSP.2018.8450799
Adler, A. & Wax, M. Constant Modulus Algorithms via Low-Rank Approximation. (2018).PDF icon CBMM-Memo-077.pdf (795.61 KB)
Adler, A. & Wax, M. Constant modulus algorithms via low-rank approximation. Signal Processing 160, 263 - 270 (2019).
Koch, C. & Tononi, G. Consciousness: here, there and everywhere?. Phil. Trans. Roy Society B 370, (2015).PDF icon Tononi & Koch '15.pdf (1.87 MB)
Dillon, M. R. & Spelke, E. S. Connecting core cognition, spatial symbols, and the abstract concepts of formal geometry. Cognitive Development Society Post-Conference, More on Development (2015).
Xiang, Y., Graeber, T., Enke, B. & Gershman, S. J. Confidence and central tendency in perceptual judgment. Attention, Perception, & Psychophysics 83, 3024 - 3034 (2021).
Goodman, N. D., Tenenbaum, J. B. & Gerstenberg, T. Concepts in a Probabilistic Language of Thought. (2014).PDF icon CBMM-Memo-010.pdf (902.53 KB)
Poggio, T., Mutch, J. & Isik, L. Computational role of eccentricity dependent cortical magnification. (2014).PDF icon CBMM-Memo-017.pdf (1.04 MB)
Gershman, S. J., Horvitz, E. J. & Tenenbaum, J. B. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines. Science 349, 273-278 (2015).
Kar, K. A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism. The Journal of Neuroscience JN-RM-2229-21 (2022). doi:10.1523/JNEUROSCI.2229-21.2022
Dehghani, N. & Wimmer, R. A computational perspective of the role of Thalamus in cognition. arxiv (2018). at <https://arxiv.org/abs/1803.00997>PDF icon ThalamusComputationArxiv.pdf (5.12 MB)
Kreiman, G. Principles of neural coding (2013).
N. Murty, A. Ratan, Bashivan, P., Abate, A., DiCarlo, J. J. & Kanwisher, N. Computational models of category-selective brain regions enable high-throughput tests of selectivity. Nature Communications 12, (2021).PDF icon s41467-021-25409-6.pdf (6.47 MB)
Chandrasekhar, V. et al. Compression of Deep Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1701.04923>PDF icon 1701.04923.pdf (614.33 KB)
Zisselman, E., Adler, A. & Elad, M. Handbook of Numerical Analysis 19, 3 - 17 (Elsevier, 2018).
Poggio, T. & Fraser, M. Compositional Sparsity of Learnable Functions. (2024).PDF icon This is an update of the AMS paper (230.72 KB)
Poggio, T. & Fraser, M. Compositional sparsity of learnable functions. Bulletin of the American Mathematical Society 61, 438-456 (2024).

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