Publications

CBMM Memos were established in 2014 as a mechanism for our center to share research results with the wider scientific community. Click here to read more about the memos and to see a full list of the memos.

Videos
Support Us

Publication

Search

Show only items where

Author

Type

Term

Year

Keyword

Found 195 results

Author Title Type [ Year

]

Filters: First Letter Of Last Name is G [Clear All Filters]

2017

Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science 73, (2017).

Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science 73, (2017).

Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science (2017).

eye_tracking_causality.pdf (8.04 MB)

Gerstenberg, T., Peterson, M. F., Goodman, N. D., Lagnado, D. A. & Tenenbaum, J. B. Eye-Tracking Causality. Psychological Science (2017).

eye_tracking_causality.pdf (8.04 MB)

Gerstenberg, T., Zhou, L., Smith, K. A. & Tenenbaum, J. B. Faulty Towers: A counterfactual simulation model of physical support. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).

Faulty Towers A counterfactual simulation model of physical support, Gerstenberg et al., 2017.pdf (8.75 MB)

Tsividis, P., Pouncy, T., Xu, J. L., Tenenbaum, J. B. & Gershman, S. J. Human Learning in Atari. AAAI Spring Symposium Series (2017).

Tsividis et al - Human Learning in Atari.pdf (844.47 KB)

Han, Y., Roig, G., Geiger, G. & Poggio, T. On the Human Visual System Invariance to Translation and Scale. Vision Sciences Society (2017).

Han, Y., Roig, G., Geiger, G. & Poggio, T. Is the Human Visual System Invariant to Translation and Scale?. AAAI Spring Symposium Series, Science of Intelligence (2017).

Wu, J., Lu, E., Kohli, P., Freeman, W. T. & Tenenbaum, J. B. Learning to See Physics via Visual De-animation. Advances in Neural Information Processing Systems 30 152–163 (2017). at <http://papers.nips.cc/paper/6620-learning-to-see-physics-via-visual-de-animation.pdf>

Learning to See Physics via Visual De-animation (1.11 MB)

Wu, J., Lu, E., Kohli, P., Freeman, W. T. & Tenenbaum, J. B. Learning to See Physics via Visual De-animation. Advances in Neural Information Processing Systems 30 152–163 (2017). at <http://papers.nips.cc/paper/6620-learning-to-see-physics-via-visual-de-animation.pdf>

Learning to See Physics via Visual De-animation (1.11 MB)

Stephan, S., Willemsen, P. & Gerstenberg, T. Marbles in inaction: Counterfactual simulation and causation by omission. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).

Marbles in Inaction Counterfactual Simulation and Causation by Omission, Stephan, Willemsen, Gerstenberg, 2017.pdf (1.46 MB)

Wu, J. et al. MarrNet: 3D Shape Reconstruction via 2.5D Sketches. Advances in Neural Information Processing Systems 30 540–550 (2017). at <http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf>

MarrNet: 3D Shape Reconstruction via 2.5D Sketches (6.25 MB)

Wu, J. et al. MarrNet: 3D Shape Reconstruction via 2.5D Sketches. Advances in Neural Information Processing Systems 30 540–550 (2017). at <http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf>

MarrNet: 3D Shape Reconstruction via 2.5D Sketches (6.25 MB)

Zhang, C. et al. Musings on Deep Learning: Properties of SGD. (2017).

CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)

CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)

CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)

Grossman, N. et al. Noninvasive Deep Brain Stimulation via Temporally Interfering Electric Fields. Cell 169, 1029 - 1041.e16 (2017).

Yildirim, I., Gerstenberg, T., Saeed, B., Toussant, M. & Tenenbaum, J. B. Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints. Proceedings of the 39th Annual Conference of the Cognitive Science Society (2017).

Physical problem solving Joint planning with symbolic, geometric, and dynamic constraints, Yildirim et al., 2017.pdf (2.46 MB)

Manek, G. et al. Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>

1707.05455.pdf (143.46 KB)

Gershman, S. J. & Daw, N. D. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Annual Review of Psychology 68, (2017).

GershmanDaw17.pdf (422.11 KB)

zhang, zhoutong et al. Shape and Material from Sound. Advances in Neural Information Processing Systems 30 1278–1288 (2017). at <http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf>

zhang, zhoutong et al. Shape and Material from Sound. Advances in Neural Information Processing Systems 30 1278–1288 (2017). at <http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf>

Golowich, N., Rakhlin, A. & Shamir, O. Size-Independent Sample Complexity of Neural Networks. (2017).

1712.06541.pdf (278.77 KB)

Zhang, C. et al. Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).

CBMM-Memo-072.pdf (3.66 MB)

Kool, W., Gershman, S. J. & Cushman, F. A. Thinking fast or slow? A reinforcement-learning approach. Society for Personality and Social Psychology (2017).

KoolEtAl_SPSP_2017.pdf (670.35 KB)

2018

Shen, W. et al. Deep Regression Forests for Age Estimation. (2018).

CBMM-Memo-085.pdf (2.2 MB)

Ullman, T. D., Stuhlmüller, A., Goodman, N. D. & Tenenbaum, J. B. Learning physical parameters from dynamic scenes. Cognitive Psychology 104, 57-82 (2018).

T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)

Pages