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.

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Meyers, E. Review of the CBMM workshop on the Turing++ Question: 'who is there?'. (2016).

Review of the CBMM workshop on the Turing++ Question- 'who is there?' .pdf (555.71 KB)

Wang, C., Wang, Y., Lin, Z., Yuille, A. & Gao, W. Robust Estimation of 3D Human Poses from a Single Image. (2014).

CBMM-Memo-013.pdf (510.23 KB)

Casper, S., Nadeau, M., Hadfield-Menell, D. & Kreiman, G. Robust Feature-Level Adversaries are Interpretability Tools. NeurIPS (2022). at <https://openreview.net/forum?id=lQ--doSB2o>

8789_robust_feature_level_adversari.pdf (3.79 MB)

Gaziv, G., Lee, M. J. & DiCarlo, J. J. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv (2023). at <https://arxiv.org/abs/2308.06887>

Cheney, N., Schrimpf, M. & Kreiman, G. On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations. (2017).

CBMM-Memo-065.pdf (687.76 KB)

Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).

CBMM-Memo-009.pdf (4.21 MB)

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Montagna, F., Noceti, N., Rosasco, L., Zhang, K. & Locatello, F. Scalable Causal Discovery with Score Matching. NeurIPS 2022 (2022). at <https://openreview.net/forum?id=v56PHv_W2A>

Han, Y., Roig, G., Geiger, G. & Poggio, T. Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020).

s41598-019-57261-6.pdf (1.46 MB)

Wang, Y. - S., Liu, C., Zeng, X. & Yuille, A. Scene Graph Parsing as Dependency Parsing. (2018).

CBMM-Memo-082.pdf (869 KB)

Ren, Z., Wang, C. & Yuille, A. Scene-Domain Active Part Models for Object Representation. IEEE International Conference on Computer Vision (ICCV) 2497 - 2505 (2015). doi:10.1109/ICCV.2015.287

Ren_ICCV15.pdf (3.37 MB)

Koch, C. & Poggio, T. A Science of Intelligence . (2015).

A Science of Intelligence.pdf (659.5 KB)

Cusimano, M., Traer, J. & McDermott, J. H. Scrape, rub, and roll: causal inference in the perception of sustained contact sounds . Cognitive Science (2019).

Owaki, T. et al. Searching for visual features that explain response variance of face neurons in inferior temporal cortex. PLOS ONE 13, e0201192 (2018).

Li, Y., Koch, C., Rehg, J. M. & Yuille, A. The Secrets of Salient Object Segmentation. (2014).

CBMM-Memo-014.pdf (1.59 MB)

Fazeli, N. et al. See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion. Science Robotics 4, eaav3123 (2019).

N. Murty, A. Ratan & Arun, S. P. Seeing a straight line on a curved surface: decoupling of patterns from surfaces by single IT neurons. Journal of Neurophysiology 11773, 104 - 116 (2017).

Arcaro, M. J., Schade, P. F., Vincent, J. L., Ponce, C. R. & Livingstone, M. S. Seeing faces is necessary for face-domain formation. Nature Neuroscience 5631628, (2017).

Barbu, A. et al. Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).

Barbu, A. et al. Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions. (2014).

CBMM Memo 012.pdf (678.95 KB)

Siddharth, N., Barbu, A. & Siskind, J. Mark. Seeing What You’re Told: Sentence-Guided Activity Recognition In Video. CVPR (IEEE, 2014).

Publication (453.54 KB)

Siddharth, N., Barbu, A. & Siskind, J. Mark. Seeing What You’re Told: Sentence-Guided Activity Recognition In Video. (2014).

CBMM-Memo-006.pdf (1.2 MB)

Siddharth, N., Barbu, A. & Siskind, J. Mark. Seeing what you're told, sentence guided activity recognition in video. Appeared at CVPR (2014).

poster-1701.pdf (4.61 MB)

Hicks, J. M. & McDermott, J. H. Segregation from Noise as Outlier Detection . Association for Research in Otolaryngology (2020).

Kosakowski, H. L. et al. Selective responses to faces, scenes, and bodies in the ventral visual pathway of infants. Current Biology 32, (2021).

Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).

CBMM-Memo-152.pdf (1.84 MB)

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