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Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/Schedule?showEvent=21868>
From Associative Memories to Powerful Machines. (2021). v1.0 (1.01 MB) v1.3Section added August 6 on self attention (3.9 MB)
From Marr’s Vision to the Problem of Human Intelligence. (2021). CBMM-Memo-118.pdf (362.19 KB)
An analysis of training and generalization errors in shallow and deep networks. Neural Networks 121, 229 - 241 (2020).
Biologically Inspired Mechanisms for Adversarial Robustness. (2020). CBMM_Memo_110.pdf (3.14 MB)
Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020). s41467-020-14663-9.pdf (431.68 KB)
CUDA-Optimized real-time rendering of a Foveated Visual System. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://arxiv.org/abs/2012.08655> Foveated_Drone_SVRHM_2020.pdf (13.44 MB) v1 (12/15/2020) (14.7 MB)
Dreaming with ARC. Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020 (2020). CBMM Memo 113.pdf (1019.64 KB)
Explicit regularization and implicit bias in deep network classifiers trained with the square loss. arXiv (2020). at <https://arxiv.org/abs/2101.00072>
For interpolating kernel machines, the minimum norm ERM solution is the most stable. (2020). CBMM_Memo_108.pdf (1015.14 KB) Better bound (without inequalities!) (1.03 MB)
Function approximation by deep networks. Communications on Pure & Applied Analysis 19, 4085 - 4095 (2020). 1534-0392_2020_8_4085.pdf (514.57 KB)
Hierarchically Local Tasks and Deep Convolutional Networks. (2020). CBMM_Memo_109.pdf (2.12 MB)
Implicit dynamic regularization in deep networks. (2020). v1.2 (2.29 MB) v.59 Update on rank (2.43 MB)
Loss landscape: SGD has a better view. (2020). CBMM-Memo-107.pdf (1.03 MB) Typos and small edits, ver11 (955.08 KB) Small edits, corrected Hessian for spurious case (337.19 KB)
An Overview of Some Issues in the Theory of Deep Networks. IEEJ Transactions on Electrical and Electronic Engineering 15, 1560 - 1571 (2020).
Scale and translation-invariance for novel objects in human vision. Scientific Reports 10, (2020). s41598-019-57261-6.pdf (1.46 MB)
Stable Foundations for Learning: a framework for learning theory (in both the classical and modern regime). (2020). Original file (584.54 KB) Corrected typos and details of "equivalence" CV stability and expected error for interpolating machines. Added Appendix on SGD. (905.29 KB) Edited Appendix on SGD. (909.19 KB) Deleted Appendix. Corrected typos etc (880.27 KB) Added result about square loss and min norm (898.03 KB)
Theoretical issues in deep networks. Proceedings of the National Academy of Sciences 201907369 (2020). doi:10.1073/pnas.1907369117 PNASlast.pdf (915.3 KB)
An analysis of training and generalization errors in shallow and deep networks. (2019). CBMM-Memo-098.pdf (687.36 KB) CBMM Memo 098 v4 (08/2019) (2.63 MB)
Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations, (ICLR 2019) (2019). gk7779.pdf (721.53 KB)
Deep Recurrent Architectures for Seismic Tomography. 81st EAGE Conference and Exhibition 2019 (2019).
Double descent in the condition number. (2019). Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB) Incorporated footnote in text plus other edits (854.05 KB) Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB) correcting a bad typo (261.24 KB) Deleted plot of condition number of kernel matrix: we cannot get a double descent curve (769.32 KB)
Dynamics & Generalization in Deep Networks -Minimizing the Norm. NAS Sackler Colloquium on Science of Deep Learning (2019).
Eccentricity Dependent Neural Network with Recurrent Attention for Scale, Translation and Clutter Invariance . Vision Science Society (2019).