Publication

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2015
Spokes, A. C. Infants’ Reasoning about Affiliation and Caregiving. Cognitive Development Society (CDS) Biennial Meeting (2015).
Dillon, M. R., Izard, V. & Spelke, E. S. Infants’ sensitivity to shape changes. Cognitive Development Society Pre-Conference on the Development of Spatial Thinking (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)
Tsividis, P., Gershman, S. J., Tenenbaum, J. B. & Schulz, L. Information Selection in Noisy Environments with Large Action Spaces. 9th Biennial Conference of the Cognitive Development Society Columbus, OH, (2015).
Meyers, E., Borzello, M., Freiwald, W. A. & Tsao, D. Intelligent Information Loss: The Coding of Facial Identity, Head Pose, and Non-Face Information in the Macaque Face Patch System. The Journal of Neuroscience 35, (2015).
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. 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)

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