Training hierarchical networks for function approximation by Miranda, Brando, M. Eng. Massachusetts Institute of Technology

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Classical generalization bounds are surprisingly tight for Deep Networks by Liao, Qianli, Miranda, Brando, Hidary, Jack, Poggio, Tomaso

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Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality? by Poggio, Tomaso, Mhaskar, Hrushikesh, Rosasco, Lorenzo, Miranda, Brando, Liao, Qianli

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Theory IIIb: Generalization in Deep Networks by Poggio, Tomaso, Liao, Qianli, Miranda, Brando, Burbanski, Andrzej, Hidary, Jack

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Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review by Mhaskar, Hrushikesh, Rosasco, Lorenzo, Miranda, Brando, Liao, Qianli, Poggio, Tomaso A

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Theory of Deep Learning IIb: Optimization Properties of SGD by Zhang, Chiyuan, Liao, Qianli, Rakhlin, Alexander, Miranda, Brando, Golowich, Noah, Poggio, Tomaso

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Musings on Deep Learning: Properties of SGD by Zhang, Chiyuan, Liao, Qianli, Rakhlin, Alexander, Sridharan, Karthik, Miranda, Brando, Golowich, Noah, Poggio, Tomaso

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Theory of Deep Learning III: explaining the non-overfitting puzzle by Poggio, Tomaso, Kawaguchi, Kenji, Liao, Qianli, Miranda, Brando, Rosasco, Lorenzo, Boix, Xavier, Hidary, Jack, Mhaskar, Hrushikesh

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High-performance and scalable on-chip digital Fourier transform spectroscopy by Kita, Derek M, Miranda, Brando, Favela, David, Bono, David, Michon, Jérôme, Lin, Hongtao, Gu, Tian, Hu, Juejun

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Chip-scale high-performance digital Fourier Transform (dFT) spectrometers by Kita, Derek M., Miranda, Brando, Ríos, Carlos, Favela, David, Bono, David C, Michon, Jerome, Lin, Hongtao, Gu, Tian, Hu, Juejun

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