All Publications
2020
“Gross means Great”, Progress in Neurobiology, vol. 195, p. 101924, 2020. ,
CBMM Related
“Social interaction networks in the primate brain”, Current Opinion in Neurobiology, vol. 65, pp. 49 - 58, 2020. ,
CBMM Funded
“CUDA-Optimized real-time rendering of a Foveated Visual System”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020, 2020.
Foveated_Drone_SVRHM_2020.pdf (13.44 MB)
v1 (12/15/2020) (14.7 MB) ,


CBMM Funded
“Explicit regularization and implicit bias in deep network classifiers trained with the square loss”, arXiv, 2020. ,
CBMM Funded
“Face selective patches in marmoset frontal cortexAbstract”, Nature Communications, vol. 11, no. 1, 2020. ,
CBMM Related
CBMM Memo No.
113
“Dreaming with ARC”, Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020. 2020.
CBMM Memo 113.pdf (1019.64 KB) ,

CBMM Funded
“Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence”, Neuron, vol. 108, no. 3, pp. 413 - 423, 2020. ,
CBMM Related
“Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, 2020.
PIIS0896627320307595.pdf (3.92 MB) ,

CBMM Funded
“An Overview of Some Issues in the Theory of Deep Networks”, IEEJ Transactions on Electrical and Electronic Engineering, vol. 15, no. 11, pp. 1560 - 1571, 2020. ,
CBMM Funded
“Hierarchical structure is employed by humans during visual motion perception”, Proceedings of the National Academy of Sciences, vol. 117, no. 39, pp. 24581 - 24589, 2020. ,
CBMM Related
CBMM Memo No.
112
“Implicit dynamic regularization in deep networks”. 2020.
v1.2 (2.29 MB)
v.59 Update on rank (2.43 MB) ,


CBMM Funded
“Function approximation by deep networks”, Communications on Pure & Applied Analysis, vol. 19, no. 8, pp. 4085 - 4095, 2020.
1534-0392_2020_8_4085.pdf (514.57 KB) ,

CBMM Funded
“The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys”, Nature Communications, vol. 11, no. 1, 2020.
s41467-020-17714-3.pdf (25.01 MB) ,

CBMM Funded
CBMM Memo No.
111
“On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations”. 2020.
CBMM-Memo-111.pdf (9.76 MB) ,

CBMM Funded
CBMM Memo No.
107
“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) ,



CBMM Funded
CBMM Memo No.
110
“Biologically Inspired Mechanisms for Adversarial Robustness”. 2020.
CBMM_Memo_110.pdf (3.14 MB) ,

CBMM Funded
CBMM Memo No.
109
“Hierarchically Local Tasks and Deep Convolutional Networks”. 2020.
CBMM_Memo_109.pdf (2.12 MB) ,

CBMM Funded
CBMM Memo No.
108
“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) ,


CBMM Funded
CBMM Memo No.
105
“Do Neural Networks for Segmentation Understand Insideness?”. 2020.
CBMM-Memo-105.pdf (4.63 MB)
CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)
CBMM Memo 105 v3 (January 25, 2022) (8.33 MB) ,



CBMM Funded
“A neural network trained for prediction mimics diverse features of biological neurons and perception”, Nature Machine Intelligence, vol. 2, no. 4, pp. 210 - 219, 2020. ,
CBMM Funded
CBMM Memo No.
103
“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) ,





CBMM Funded
“Evidence that recurrent pathways between the prefrontal and inferior temporal cortex is critical during core object recognition ”, in COSYNE, Denver, Colorado, USA, 2020. ,
CBMM Funded
“Hierarchical neural network models that more closely match primary visual cortex tend to better explain higher level visual cortical responses ”, in COSYNE, Denver, Colorado, USA, 2020. ,
CBMM Funded
“Complexity Control by Gradient Descent in Deep Networks”, Nature Communications, vol. 11, 2020.
s41467-020-14663-9.pdf (431.68 KB) ,

CBMM Funded
“Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates ”, in COSYNE, Denver, CO, USA, 2020. ,
CBMM Funded