Export 652 results:
How, whether, why: Causal judgments as counterfactual contrasts. Annual Meeting of the Cognitive Science Society (CogSci) 782-787 (2015). at <https://mindmodeling.org/cogsci2015/papers/0142/index.html>
Human-level concept learning through probabilistic program induction. Science 350, 1332-1338 (2015).
Hypothesis-Space Constraints in Causal Learning. Annual Meeting of the Cognitive Science Society (CogSci) (2015). at <https://mindmodeling.org/cogsci2015/papers/0418/index.html>
Imagination and the generation of new ideas. Cognitive Development 34, 99–110 (2015).
The Infancy of the Human Brain. Neuron 88, 93 - 109 (2015).
Infants’ Categorization of Social Actions. Cognitive Development Society (CDS) (2015).
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).
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).
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015).
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).
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>
Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439>
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>
Learning with incremental iterative regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/6015-learning-with-incremental-iterative-regularization>
Less is More: Nyström Computational Regularization. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5936-less-is-more-nystrom-computational-regularization>