Publication

Found 283 results
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2015
Powell, L. J. & Spelke, E. S. Infants’ Categorization of Social Actions. Cognitive Development Society (CDS) (2015).
Linderman, S. W., Adams, R. & Pillow, J. Inferring structured connectivity from spike trains under negative-binomial generalized linear models. (2015).PDF icon cosyne2015a.pdf (384.83 KB)
Anselmi, F., Rosasco, L. & Poggio, T. On Invariance and Selectivity in Representation Learning. (2015).PDF icon CBMM Memo No. 029 (812.07 KB)
Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015).Binary Data modularity_dataset_ver1.tar.gz (36.14 MB)
Leibo, J. Z., Liao, Q., Anselmi, F. & Poggio, T. The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015).PDF icon journal.pcbi_.1004390.pdf (2.04 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
Isik, L., Tacchetti, A. & Poggio, T. Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).
Poggio, T., Anselmi, F. & Rosasco, L. I-theory on depth vs width: hierarchical function composition. (2015).PDF icon cbmm_memo_041.pdf (1.18 MB)
Frogner, C., Zhang, C., Mobahi, H., Araya-Polo, M. & Poggio, T. Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439>PDF icon Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)
Mroueh, Y., Voinea, S. & Poggio, T. Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective>PDF icon LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
Poggio, T., Rosasco, L., Shashua, A., Cohen, N. & Anselmi, F. Notes on Hierarchical Splines, DCLNs and i-theory. (2015).PDF icon CBMM Memo 037 (1.83 MB)
Newman, J. P. et al. Optogenetic feedback control of neural activity. Elife 4, e07192 (2015).PDF icon elife-07192-v1-download.pdf (5.92 MB)
Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).PDF icon 2015 SFN Population_Coding.pdf (2.94 MB)
Buice, M. & de Vries, S. Population Coding, Correlations, and Functional Connectivity in the mouse visual system with the Cortical Activity Map (CAM). Society for Neuroscience 2015 (2015).PDF icon 2015 SFN Population_Coding.pdf (2.94 MB)
Kosakowski, H. L., Powell, L. J. & Spelke, E. S. Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors. CBMM Summer Research Program (2015).PDF icon Preverbal Infants' Third-Party Imitator Preferences: Animated Displays versus Filmed Actors (46.32 MB)
Koch, C. & Poggio, T. A Science of Intelligence . (2015).PDF icon A Science of Intelligence.pdf (659.5 KB)
Anselmi, F. et al. Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048
Powell, L. J., Deen, B., Guo, L. & Saxe, R. Using fNIRS to Map Functional Specificity in the Infant Brain: An fROI Approach. (2015).PDF icon SRCD2015_NIRS_poster.pdf (2.14 MB)
Poggio, T. What if.. (2015).PDF icon What if.pdf (2.09 MB)
2014
Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).PDF icon CBMM-Memo-024.pdf (2.86 MB)
Amir, N. et al. Abstracts of the 2014 Brains, Minds, and Machines Summer Course. (2014).PDF icon CBMM-Memo-024.pdf (2.86 MB)
Liao, Q., Leibo, J. Z., Mroueh, Y. & Poggio, T. Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?. (2014).PDF icon CBMM-Memo-003.pdf (963.66 KB)
Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).PDF icon CBMM Memo 011.pdf (3.95 MB)
Poggio, T., Mutch, J. & Isik, L. Computational role of eccentricity dependent cortical magnification. (2014).PDF icon CBMM-Memo-017.pdf (1.04 MB)
Zhang, C., Evangelopoulos, G., Voinea, S., Rosasco, L. & Poggio, T. A Deep Representation for Invariance And Music Classification. (2014).PDF icon CBMM-Memo-002.pdf (1.63 MB)

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