Publication

Found 904 results
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Bass, I., Smith, K. A., Bonawitz, E. & Ullman, T. Partial Mental Simulation Explains Fallacies in Physical Reasoning. psyArXiv (2021). at <https://psyarxiv.com/y4a8x>
Bashivan, P., Kar, K. & DiCarlo, J. J. Neural Population Control via Deep Image Synthesis. Science 364, (2019).PDF icon Author's last draft (18.45 MB)
Bardon, A., Xiao, W., Ponce, C. R., Livingstone, M. S. & Kreiman, G. Face neurons encode nonsemantic features. Proceedings of the National Academy of Sciences 119, (2022).
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. Seeing is Worse than Believing: Reading People’s Minds Better than Computer-Vision Methods Recognize Actions. (2014).PDF icon CBMM Memo 012.pdf (678.95 KB)
Barbu, A. et al. Computer Vision – ECCV 2014, Lecture Notes in Computer Science 8693, 612–627 (Springer International Publishing, 2014).
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).PDF icon 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)
Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).PDF icon CBMM Memo 011.pdf (3.95 MB)
Bansal, A. et al. Neural Dynamics Underlying Target Detection in the Human Brain. Journal of Neuroscience 34, (2014).
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. Dreaming with ARC. Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020 (2020).PDF icon CBMM Memo 113.pdf (1019.64 KB)
Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).
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).PDF icon CBMM Memo 115.pdf (9.56 MB)PDF icon arXiv version (23.05 MB)
Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).PDF icon Original, intermediate versions are available under request (2.67 MB)PDF icon CBMM Memo 90 v12.pdf (4.74 MB)PDF icon Theory_III_ver44.pdf Update Hessian (4.12 MB)PDF icon Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)PDF icon fixing errors and sharpening some proofs (2.45 MB)
Baldauf, D. & Desimone, R. Neural Mechanisms of Object-Based Attention. Science 344, 424 - 427 (2014).
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).PDF icon article.pdf (2.17 MB)
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>
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>
Bach, F. & Poggio, T. Introduction Special issue: Deep learning. Information and Inference 5, 103-104 (2016).
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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).
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).
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).PDF icon Atabaki Marciniak Dicke Thier 2015 Vis Res Assesing the precision of gaze following using a stereoscopic 3D virtual reality setting.pdf (2.52 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
Arend, L. et al. Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results. (2018).PDF icon CBMM-Memo-093.pdf (2.99 MB)

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