Publication

Found 332 results
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2015
Magid, R. Imagination and the generation of new ideas. Cognitive Development 34, 99–110 (2015).PDF icon Imagination and the generation of new ideas (266.63 KB)
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).
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)
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)
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)
Kulkarni, T., Kohli, P., Tenenbaum, J. B. & Mansinghka, V. Picture: An Imperative Probabilistic Programming Language for Scene Perception. Computer Vision and Pattern Recognition (2015).
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)
Magid, R. & Schulz, L. Quit while you’re ahead: Preschoolers’ persistence and willingness to accept challenges are affected by social comparison. Annual Meeting of the Cognitive Science Society (CogSci) (2015).PDF icon 15_Cogsci_Magid&Schulz.pdf (513.72 KB)
Nickel, M., Murphy, K., Tresp, V. & Gabrilovich, E. A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction. (2015).PDF icon CBMM Memo No. 028 (878.56 KB)
Singer, J., Madsen, J., Anderson, W. S. & Kreiman, G. Sensitivity to timing and order in human visual cortex. Journal of Neurophysiology 113, 1656 - 1669 (2015).
Anselmi, F. et al. Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Mechanisms of Sensory Working Memory: Attention and Performance XXV. (Elsevier Inc. , 2015). at <https://www.sciencedirect.com/book/9780128013717/mechanisms-of-sensory-working-memory>
Mendoza-Halliday, D., Torres, S. & Martinez-Trujillo, J. Mechanisms of Sensory Working Memory: Attention and Performance XXV. (Elsevier Inc. , 2015). at <https://www.sciencedirect.com/book/9780128013717/mechanisms-of-sensory-working-memory>
2016
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352PDF icon Manuscript  (1.83 MB)
Mhaskar, H. & Poggio, T. Deep vs. shallow networks : An approximation theory perspective. (2016).PDF icon Original submission, visit the link above for the updated version (960.27 KB)
Mhaskar, H. & Poggio, T. Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).
Traer, J. & McDermott, J. H. Environmental statistics enable perceptual separation of sound and space. Speech and Audio in the Northeast (2016).
Fischer, J., Mikhael, J. G., Tenenbaum, J. B. & Kanwisher, N. Functional neuroanatomy of intuitive physical inference. Proceedings of the National Academy of Sciences 113, E5072 - E5081 (2016).
Mao, J. et al. Generation and Comprehension of Unambiguous Object Descriptions. The Conference on Computer Vision and Pattern Recognition (CVPR) (2016). at <https://github.com/ mjhucla/Google_Refexp_toolbox>PDF icon object_description_cbmm.pdf (2.21 MB)
Mao, J. et al. Generation and Comprehension of Unambiguous Object Descriptions. The Conference on Computer Vision and Pattern Recognition (CVPR) (2016). at <https://github.com/ mjhucla/Google_Refexp_toolbox>PDF icon object_description_cbmm.pdf (2.21 MB)
Morère, O. et al. Group Invariant Deep Representations for Image Instance Retrieval. (2016).PDF icon CBMM-Memo-043.pdf (2.66 MB)

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