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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.

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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, 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, 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, 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, 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., 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)

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

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., 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, 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)

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. (2018).

CBMM-Memo-092.pdf (1.31 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)

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, M. et al. The Janus effects of SGD vs GD: high noise and low rank. (2023).

Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)

Small updates...typos... (616.82 KB)

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>

Xu, M., Rangamani, A., Liao, Q., Galanti, T. & Poggio, T. Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds. Research (2023). doi:10.34133/research.0024

research.0024.pdf (4.05 MB)

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