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 904 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

A

Anzellottti, S., Houlihan, S. Dae, Liburd, Jr, S. & Saxe, R. Leveraging facial expressions and contextual information to investigate opaque representations of emotions. Emotion (2021). doi:10.1037/emo0000685

Anzellotti 2021 Emotion.pdf (1.08 MB)

Araya-Polo, M., Adler, A., Farris, S. & Jennings, J. Deep Learning: Algorithms and Applications (SPRINGER-VERLAG, 2019).

Araya-Polo, M., Jennings, J., Adler, A. & Dahlke, T. Deep-learning tomography. The Leading Edge 37, 58 - 66 (2018).

TLE2018.pdf (1.9 MB)

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).

Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).

CBMM-Memo-093.pdf (2.99 MB)

Armendariz, M., Xiao, W., Vinken, K. & Kreiman, G. Do computational models of vision need shape-based representations? Evidence from an individual with intriguing visual perceptions. Cognitive Neuropsychology 1 - 3 (2022). doi:10.1080/02643294.2022.2041588

Atabaki, A., Marciniak, K., Dicke, P. W. & Thier, P. Assessing the precision of gaze following using a stereoscopic 3D virtual reality setting. Vision Res 112, 68-82 (2015).

Atabaki Marciniak Dicke Thier 2015 Vis Res Assesing the precision of gaze following using a stereoscopic 3D virtual reality setting.pdf (2.52 MB)

Xu, C. et al. Lecture Notes in Computer ScienceComputer Vision – ECCV 2022Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models. 13697, 638 - 656 (Springer Nature Switzerland, 2022).

Azami, H. et al. EEG Entropy in REM Sleep as a Physiologic Biomarker in Early Clinical Stages of Alzheimer’s Disease. Journal of Alzheimer's Disease 91, 1557 - 1572 (2023).

B

Bach, F. & Poggio, T. Introduction Special issue: Deep learning. Information and Inference 5, 103-104 (2016).

Bagus, A. Marliawaty, Marques, T., Sanghavi, S., DiCarlo, J. J. & Schrimpf, M. Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units. NeurIPS (2022). at <https://openreview.net/forum?id=iPF7mhoWkOl>

Baidya, A., Dapello, J., DiCarlo, J. J. & Marques, T. Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/ScheduleMultitrack?event=41268>

Baker, C., Jara-Ettinger, J., Saxe, R. & Tenenbaum, J. B. Rational quantitative attribution of beliefs, desires, and percepts in human mentalizing. Nature Human Behavior 1, (2017).

article.pdf (2.17 MB)

Baldauf, D. & Desimone, R. Neural Mechanisms of Object-Based Attention. Science 344, 424 - 427 (2014).

Banburski, A. et al. Dynamics & Generalization in Deep Networks -Minimizing the Norm. NAS Sackler Colloquium on Science of Deep Learning (2019).

Banburski, A., De La Torre, F., Pant, N., Shastri, I. & Poggio, T. Distribution of Classification Margins: Are All Data Equal?. (2021).

CBMM Memo 115.pdf (9.56 MB)

arXiv version (23.05 MB)

Banburski, A. & Rangamani, A. Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay. IEEE International Conference on Acoustics, Speech and Signal Processing (2022).

Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).

Original, intermediate versions are available under request (2.67 MB)

CBMM Memo 90 v12.pdf (4.74 MB)

Theory_III_ver44.pdf Update Hessian (4.12 MB)

Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)

fixing errors and sharpening some proofs (2.45 MB)

Banburski, A. et al. Dreaming with ARC. Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020 (2020).

CBMM Memo 113.pdf (1019.64 KB)

Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).

Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).

Barbu, A., Banda, D. & Katz, B. Deep video-to-video transformations for accessibility with an application to photosensitivity. Pattern Recognition Letters (2019). doi:10.1016/j.patrec.2019.01.019

Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).

CBMM Memo 011.pdf (3.95 MB)

Barbu, A. et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Neural Information Processing Systems (NeurIPS 2019) (2019).

9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)

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)

Pages