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 906 results

] Title Type Year

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

W

Woo, B. & Spelke, E. Eight-Month-Old Infants’ Social Evaluations of Agents Who Act on False Beliefs. Proceedings of the Annual Meeting of the Cognitive Science Society 44, (2022).

Woo, B. M. & Spelke, E. S. Toddlers’ social evaluations of agents who act on false beliefs. Developmental Science 26, (2022).

Wu, Y. & Schulz, L. A fine-grained understanding of emotions: Young children match within-valence emotional expressions to their causes. Cognitive Science Conference (CogSci) 2685-2690 (2015).

Cogsci Emotion pairings 2-4-15 Final version.pdf (729.07 KB)

Wu, K., Wu, E. & Kreiman, G. Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition. arXiv | Cornell University arXiv:1803.01967, (2018).

Wu, Y. & Schulz, L. Inferring Beliefs and Desires From Emotional Reactions to Anticipated and Observed Events. Child Development (2017). doi:10.1111/cdev.12759

Wu_et_al-2017-Child_Development.pdf (883.1 KB)

Wu, Y., Muentener, P. & Schulz, L. The invisible hand: Toddlers connect probabilistic events with agentive causes. Cognitive Science 40, 23 (2016).

Wu_Muentener_Schulz_2016_InvisibleHand.pdf (307.21 KB)

Wu, Y., Muentener, P. & Schulz, L. One- to Four-year-olds’ Ability to Connect Diverse Positive Emotional Expressions to Their Probable Causes . Society for Research in Child Development (2017).

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, Y., Baker, C., Tenenbaum, J. B. & Schulz, L. Rational inference of beliefs and desires from emotional expressions. Cognitive Science 42, (2018).

Wu_Baker_Tenenbaum_Schulz_in_press_cognitive_science.pdf (1.65 MB)

Wu, J., Yildirim, I., Lim, J. J., Freeman, W. T. & Tenenbaum, J. B. Galileo: Perceiving physical object properties by integrating a physics engine with deep learning. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5780-galileo-perceiving-physical-object-properties-by-integrating-a-physics-engine-with-deep-learning>

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)

X

Xia, F., Wang, P., Chen, L. -chieh & Yuille, A. Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net. ECCV (2016).

Xia, F., Wang, P., Chen, L. -chieh & Yuille, A. Zoom better to see clearer: Human and object parsing with hierarchical auto-zoom net. ECCV (2016).

auto-zoom_net.pdf (5.77 MB)

Xiang, Y., Graeber, T., Enke, B. & Gershman, S. J. Confidence and central tendency in perceptual judgment. Attention, Perception, & Psychophysics 83, 3024 - 3034 (2021).

Xiang, Y., Vélez, N. & Gershman, S. J. Collaborative decision making is grounded in representations of other people’s competence and effort. Journal of Experimental Psychology: General 152, 1565 - 1579 (2023).

Xiang, Y., Landy, J., Cushman, F. A., Vélez, N. & Gershman, S. J. Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks. Cognition 241, 105609 (2023).

Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. (2018).

CBMM-Memo-092.pdf (1.31 MB)

Xiao, W., Sharma, S., Kreiman, G. & Livingstone, M. S. Out of sight, out of mind: Responses in primate ventral visual cortex track individual fixations during natural vision. bioRxiv (2023). doi:10.1101/2023.02.08.527666

Xiao, Y. et al. Cross-task specificity and within-task invariance of cognitive control processes. Cell Reports 42, 111919 (2023).

PIIS2211124722018174.pdf (3.97 MB)

Xiao, Y. et al. Task-specific neural processes underlying conflict resolution during cognitive control. BioRxiv (2022). doi:10.1101/2022.01.16.476535

2022.01.16.476535v1.full_.pdf (22.96 MB)

Xiao, W., Chen, H., Liao, Q. & Poggio, T. Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations, (ICLR 2019) (2019).

gk7779.pdf (721.53 KB)

Xiao, W. & Kreiman, G. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020).

gk7791.pdf (2.39 MB)

Xie, Y., Li, Y. & Rangamani, A. Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms. (2023).

CBMM-Memo-142.pdf (1.64 MB)

Xu, M. et al. Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss. (2021).

v1.0 (4.61 MB)

v1.4corrections to generalization section (5.85 MB)

v1.7Small edits (22.65 MB)

Xu, J., Jiang, M., Wang, S., Kankanhalli, M. & Zhao, Q. Predicting Saliency Beyond Pixels. (2014). at <http://www.ece.nus.edu.sg/stfpage/eleqiz/predicting.html>

Pages