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Explicit regularization and implicit bias in deep network classifiers trained with the square loss


Dec 31, 2020
Tomaso Poggio, Qianli Liao


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Hierarchically Local Tasks and Deep Convolutional Networks


Jun 29, 2020
Arturo Deza, Qianli Liao, Andrzej Banburski, Tomaso Poggio

* A pre-print. Submitted to the Conference of Neural Information Processing Systems (NeurIPS) 2020 

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Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization


Aug 25, 2019
Tomaso Poggio, Andrzej Banburski, Qianli Liao

* arXiv admin note: text overlap with arXiv:1611.00740 

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Theory III: Dynamics and Generalization in Deep Networks - a simple solution


Apr 11, 2019
Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Bob Liang, Jack Hidary, Tomaso Poggio

* 50 pages, 11 figures. This replaces previous versions of Theory III, that appeared on Arxiv [arXiv:1806.11379, arXiv:1801.00173] or on the CBMM site. v2: Some arguments in sections 3, 7 and the appendix have been strengthened, in particular an observation on intrinsic normalization of standard gradient descent 

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Biologically-plausible learning algorithms can scale to large datasets


Nov 25, 2018
Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio


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A Surprising Linear Relationship Predicts Test Performance in Deep Networks


Jul 25, 2018
Qianli Liao, Brando Miranda, Andrzej Banburski, Jack Hidary, Tomaso Poggio


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Theory IIIb: Generalization in Deep Networks


Jun 29, 2018
Tomaso Poggio, Qianli Liao, Brando Miranda, Andrzej Banburski, Xavier Boix, Jack Hidary

* 38 pages, 7 figures 

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Theory of Deep Learning III: explaining the non-overfitting puzzle


Jan 16, 2018
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Xavier Boix, Jack Hidary, Hrushikesh Mhaskar


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Theory of Deep Learning IIb: Optimization Properties of SGD


Jan 07, 2018
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin, Brando Miranda, Noah Golowich, Tomaso Poggio


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Theory II: Landscape of the Empirical Risk in Deep Learning


Jun 22, 2017
Qianli Liao, Tomaso Poggio

* Merged figures to make the main text more compact. Moved some similar figures to the appendix 

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Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review


Feb 04, 2017
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao


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Compression of Deep Neural Networks for Image Instance Retrieval


Jan 18, 2017
Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Lingyu Duan, Tomaso Poggio

* 10 pages, accepted by DCC 2017 

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Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning


Oct 19, 2016
Qianli Liao, Kenji Kawaguchi, Tomaso Poggio


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View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation


Jun 05, 2016
Joel Z. Leibo, Qianli Liao, Winrich Freiwald, Fabio Anselmi, Tomaso Poggio


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Learning Functions: When Is Deep Better Than Shallow


May 29, 2016
Hrushikesh Mhaskar, Qianli Liao, Tomaso Poggio


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Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex


Apr 13, 2016
Qianli Liao, Tomaso Poggio


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How Important is Weight Symmetry in Backpropagation?


Feb 04, 2016
Qianli Liao, Joel Z. Leibo, Tomaso Poggio


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Unsupervised learning of clutter-resistant visual representations from natural videos


Apr 24, 2015
Qianli Liao, Joel Z. Leibo, Tomaso Poggio


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Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?


Mar 26, 2014
Qianli Liao, Joel Z Leibo, Youssef Mroueh, Tomaso Poggio

* 11 Pages, 4 Figures. Mar 26, (2014): Improved exposition. Added CBMM memo cover page. No substantive changes 

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