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2015
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)
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)
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).
Dillon, M. R., Izard, V. & Spelke, E. S. Isolating angle in infants' detection of shape. (2015).PDF icon SRCD_2015_Dillonetal.pdf (5.01 MB)
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)
Mao, J. et al. Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images. International Conference of Computer Vision (2015). at <www.stat.ucla.edu/~junhua.mao/projects/child_learning.html>PDF icon child_learning_iccv2015.pdf (1.16 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)
Rosasco, L. & Villa, S. Learning with incremental iterative regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/6015-learning-with-incremental-iterative-regularization>PDF icon Learning with Incremental Iterative Regularization_1405.0042v2.pdf (504.66 KB)
Rudi, A., Camoriano, R. & Rosasco, L. Less is More: Nyström Computational Regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization>PDF icon Less is More- Nystr ̈om Computational Regularization_1507.04717v4.pdf (287.14 KB)
Koch, C. Lust and the Turing test [Nature] . (2015). at <http://blogs.nature.com/aviewfromthebridge/2015/05/27/lust-and-the-turing-test/>PDF icon Lust and the Turing Test.pdf (203.1 KB)
Ellis, K. & Lewis, O. Metareasoning in Symbolic Domains. NIPS Workshop | Bounded Optimality and Rational Metareasoning (2015). at <https://sites.google.com/site/boundedoptimalityworkshop/>PDF icon metareasoning_submitted.pdf (491.95 KB)
Ben-Yosef, G., Assif, L., Harari, D. & Ullman, S. A model for full local image interpretation. Cognitive Science Society (2015).PDF icon Full object interpretation CogSci 2015 Print version.pdf (707.34 KB)
Winston, P. Henry. Model-based Story Summary. 6th Workshop on Computational Models of Narrative (2015). doi:10.4230/OASIcs.CMN.2015.157
Marciniak, K., Dicke, P. W. & Thier, P. Monkeys head-gaze following is fast, precise and not fully suppressible. Proc Biol Sci 282, 20151020 (2015).PDF icon Marciniak et al 2015 Proc R Soc B Monkeys head gaze following is fast precise and not fully suppressible.pdf (7.07 MB)
Scott, K. Moving the lab home: validation of a web-based system for developmental studies. (2015).
Siegle, J. H., Hale, G. J., Newman, J. P. & Voigts, J. Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology. Current Opinion in Neurobiology 32, 53 - 59 (2015).
Jara-Ettinger, J. Not So Innocent: Toddlers’ Inferences About Costs and Culpability. Psychological Science 26, 633-40 (2015).PDF icon NotSoInnocent_InPress.pdf (238.53 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)
Wong, A. & Yuille, A. One Shot Learning by Composition of Meaningful Patches. International Conference on Computer Vision (ICCV) (2015).PDF icon AlexWongOneShotCVPR2015.pdf (1.83 MB)
Wong, A. & Yuille, A. One Shot Learning via Compositions of Meaningful Patches. International Conference on Computer Vision (ICCV) (2015).PDF icon AlexWongOneShotCVPR2015.pdf (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)
Rockmore, D. Our Mother the Machine, by Dan Rockmore [Huffpost] . (2015). at <http://www.huffingtonpost.com/dan-rockmore/our-mother-the-machine_b_7273504.html>PDF icon Our Mother the Machine.pdf (199.73 KB)
Chen, X. & Yuille, A. Parsing Occluded People by Flexible Compositions. Computer Vision and Pattern Recognition (CVPR) (2015).PDF icon CBMM Memo 034.pdf (5.54 MB)

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