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Infants’ Reasoning about Affiliation and Caregiving. Cognitive Development Society (CDS) | More on Development workshop (2015).
Infants’ Reasoning about Affiliation and Caregiving. Cognitive Development Society (CDS) Biennial Meeting (2015).
Infants’ sensitivity to shape changes. Cognitive Development Society Pre-Conference on the Development of Spatial Thinking (2015).
Inferring structured connectivity from spike trains under negative-binomial generalized linear models. (2015). cosyne2015a.pdf (384.83 KB)
Information Selection in Noisy Environments with Large Action Spaces. 9th Biennial Conference of the Cognitive Development Society Columbus, OH, (2015).
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).
On Invariance and Selectivity in Representation Learning. (2015). CBMM Memo No. 029 (812.07 KB)
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015). modularity_dataset_ver1.tar.gz (36.14 MB)
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015). journal.pcbi_.1004390.pdf (2.04 MB)
Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).
Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
Isolating angle in infants' detection of shape. (2015). SRCD_2015_Dillonetal.pdf (5.01 MB)
I-theory on depth vs width: hierarchical function composition. (2015). cbmm_memo_041.pdf (1.18 MB)
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> child_learning_iccv2015.pdf (1.16 MB)
Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439> Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)
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> LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
Learning with incremental iterative regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/6015-learning-with-incremental-iterative-regularization> Learning with Incremental Iterative Regularization_1405.0042v2.pdf (504.66 KB)
Less is More: Nyström Computational Regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization> Less is More- Nystr ̈om Computational Regularization_1507.04717v4.pdf (287.14 KB)
Lust and the Turing test [Nature] . (2015). at <http://blogs.nature.com/aviewfromthebridge/2015/05/27/lust-and-the-turing-test/> Lust and the Turing Test.pdf (203.1 KB)
Metareasoning in Symbolic Domains. NIPS Workshop | Bounded Optimality and Rational Metareasoning (2015). at <https://sites.google.com/site/boundedoptimalityworkshop/> metareasoning_submitted.pdf (491.95 KB)
A model for full local image interpretation. Cognitive Science Society (2015). Full object interpretation CogSci 2015 Print version.pdf (707.34 KB)
Model-based Story Summary. 6th Workshop on Computational Models of Narrative (2015). doi:10.4230/OASIcs.CMN.2015.157.
Monkeys head-gaze following is fast, precise and not fully suppressible. Proc Biol Sci 282, 20151020 (2015). Marciniak et al 2015 Proc R Soc B Monkeys head gaze following is fast precise and not fully suppressible.pdf (7.07 MB)
Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology. Current Opinion in Neurobiology 32, 53 - 59 (2015).