Export 554 results:
Hamrick, J. B. et al. Relational inductive bias for physical construction in humans and machines. In Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci 2018) (2018).PDF icon 1806.01203.pdf (1022.51 KB)
Wang, Y. - S., Liu, C., Zeng, X. & Yuille, A. Scene Graph Parsing as Dependency Parsing. (2018).PDF icon CBMM-Memo-082.pdf (869 KB)
Owaki, T. et al. Searching for visual features that explain response variance of face neurons in inferior temporal cortex. PLOS ONE 13, e0201192 (2018).
Adhya, D. et al. Shared gene co-expression networks in autism from induced pluripotent stem cell (iPSC) neurons. BioRxiv (2018).
Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).PDF icon CBMM-Memo-093.pdf (2.99 MB)
Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. (2018).PDF icon CBMM-Memo-084.pdf (1.92 MB)
Zhang, Z. et al. Single-Shot Object Detection with Enriched Semantics. Conference on Computer Vision and Pattern Recognition (CVPR) (2018). at <>
Ben-Yosef, G., Kreiman, G. & Ullman, S. Spatiotemporal interpretation features in the recognition of dynamic images. (2018).PDF icon CBMM-Memo-094.pdf (1.21 MB)Package icon (1.8 MB)File fig1.ppsx (147.67 KB)File fig2.ppsx (419.72 KB)File fig4.ppsx (673.41 KB)File figS1.ppsx (587.88 KB)File figS2.ppsx (281.56 KB)
Hart, Y. et al. The statistical shape of geometric reasoning. Scientific Reports 8, (2018).
Lewis, O. Structured learning and inference with neural networks and generative models. (2018).
Poggio, T. & Liao, Q. Theory I: Deep networks and the curse of dimensionality. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).PDF icon 02_761-774_00966_Bpast.No_.66-6_28.12.18_K1.pdf (1.18 MB)
Poggio, T. & Liao, Q. Theory II: Deep learning and optimization. Bulletin of the Polish Academy of Sciences: Technical Sciences 66, (2018).PDF icon 03_775-788_00920_Bpast.No_.66-6_31.12.18_K2.pdf (5.43 MB)
Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).PDF icon TheoryIII_ver2 (2.67 MB)PDF icon TheoryIII_ver11 (4.17 MB)PDF icon TheoryIII_ver12 (4.74 MB)PDF icon TheoryIII_ver13 (4.75 MB)PDF icon TheoryIII_ver14 (3.89 MB)PDF icon TheoryIII_ver15 (3.9 MB)PDF icon TheoryIII_ver20 (3.91 MB)PDF icon TheoryIII_ver22 (4.97 MB)PDF icon TheoryIII_ver25 (1.19 MB)PDF icon TheoryIII_ver28 (1.17 MB)PDF icon TheoryIII_ver29 (1.17 MB)PDF icon TheoryIII_ver30 (1.17 MB)PDF icon TheoryIII_ver31 (most typos and other errors corrected in main text) (1.18 MB)
Powell, L. J. & Spelke, E. S. Third-Party Preferences for Imitators in Preverbal Infants. Open Mind 2, 61 - 71 (2018).
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Trading robust representations for sample complexity through self-supervised visual experience. Advances in Neural Information Processing Systems 31 (Bengio, S. et al.) 9640–9650 (Curran Associates, Inc., 2018). at <>PDF icon trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)PDF icon NeurIPS2018_Poster.pdf (6.12 MB)
Wang, J. et al. Visual concepts and compositional voting. (2018).PDF icon CBMM-Memo-087.pdf (3.37 MB)
Wang, J. et al. Visual Concepts and Compositional Voting. Annals of Mathematical Sciences and Applications (AMSA) 3, 151–188 (2018).
Zhang, M., Feng, J., Lim, J. Hwee, Zhao, Q. & Gabriel, K. What am I searching for?. (2018).PDF icon CBMM-Memo-096.pdf (1.74 MB)
Isik, L. et al. What is changing when: decoding visual information in movies from human intracranial recordings. NeuroImage 180, Part A, 147-159 (2018).PDF icon Human neurophysiological responses during movies (2.78 MB)