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Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).
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>
Dreaming with ARC. Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020 (2020).
Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations, (ICLR 2019) (2019).
Deep Recurrent Architectures for Seismic Tomography. 81st EAGE Conference and Exhibition 2019 (2019).
Dynamics & Generalization in Deep Networks -Minimizing the Norm. NAS Sackler Colloquium on Science of Deep Learning (2019).
Properties of invariant object recognition in human one-shot learning suggests a hierarchical architecture different from deep convolutional neural networks. Vision Science Society (2019).
Theoretical Issues in Deep Networks. (2019).
Theories of Deep Learning: Approximation, Optimization and Generalization . TECHCON 2019 (2019).
A fast, invariant representation for human action in the visual system. Journal of Neurophysiology (2018). doi:https://doi.org/10.1152/jn.00642.2017
Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science 4, 403 - 422 (2018).