| Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review T Poggio, H Mhaskar, L Rosasco, B Miranda, Q Liao International Journal of Automation and Computing 14 (5), 503-519, 2017 | 277 | 2017 |
| High-performance and scalable on-chip digital Fourier transform spectroscopy DM Kita, B Miranda, D Favela, D Bono, J Michon, H Lin, T Gu, J Hu Nature communications 9 (1), 1-7, 2018 | 70 | 2018 |
| Theory of deep learning III: explaining the non-overfitting puzzle T Poggio, K Kawaguchi, Q Liao, B Miranda, L Rosasco, X Boix, J Hidary, ... arXiv preprint arXiv:1801.00173, 2017 | 69 | 2017 |
| Theory of deep learning IIb: Optimization properties of SGD C Zhang, Q Liao, A Rakhlin, B Miranda, N Golowich, T Poggio arXiv preprint arXiv:1801.02254, 2018 | 40* | 2018 |
| Theory of deep learning iii: Generalization properties of sgd C Zhang, Q Liao, A Rakhlin, K Sridharan, B Miranda, N Golowich, ... arXiv preprint arXiv:1801.00173, 2017 | 29 | 2017 |
| A surprising linear relationship predicts test performance in deep networks Q Liao, B Miranda, A Banburski, J Hidary, T Poggio arXiv preprint arXiv:1807.09659, 2018 | 25 | 2018 |
| Theory IIIb: Generalization in deep networks T Poggio, Q Liao, B Miranda, A Banburski, X Boix, J Hidary arXiv preprint arXiv:1806.11379, 2018 | 18 | 2018 |
| Theory of deep learning iii: the non-overfitting puzzle T Poggio, K Kawaguchi, Q Liao, B Miranda, L Rosasco, X Boix, J Hidary, ... CBMM Memo. 073, 2018 | 15 | 2018 |
| Theory of deep learning III: Dynamics and generalization in deep networks A Banburski, Q Liao, B Miranda, T Poggio, L Rosasco, B Liang, J Hidary Center for Brains, Minds and Machines [CBMM], Cambridge, MA, 2019 | 10 | 2019 |
| Theory I: Why and when can deep-but not shallow-networks avoid the curse of dimensionality T Poggio, H Mhaskar, L Rosasco, B Miranda, Q Liao Center for Brains, Minds and Machines [CBMM], Cambridge, MA, 2016 | 9 | 2016 |
| Digital Fourier transform spectroscopy: a high-performance, scalable technology for on-chip spectrum analysis DM Kita, B Miranda, D Favela, D Bono, J Michon, H Lin, T Gu, J Hu arXiv preprint arXiv:1802.05270, 2018 | 8 | 2018 |
| Theory III: Dynamics and Generalization in Deep Networks1 A Banburski, Q Liao, B Miranda, T Poggio, L Rosasco, F De La Torre, ... arXiv preprint arXiv:1903.04991, 2019 | 5 | 2019 |
| Theory III: Dynamics and Generalization in Deep Networks--a simple solution A Banburski, Q Liao, B Miranda, L Rosasco, J Hidary, T Poggio arXiv preprint arXiv:1903.04991, 2019 | 4 | 2019 |
| High-resolution on-chip digital Fourier transform spectroscopy DM Kita, B Miranda, D Favela, D Bono, J Michon, H Lin, T Gu, J Hu CLEO: Science and Innovations, SF1A. 1, 2018 | 4 | 2018 |
| Classical generalization bounds are surprisingly tight for deep networks Q Liao, B Miranda, J Hidary, T Poggio Center for Brains, Minds and Machines (CBMM), 2018 | 2 | 2018 |
| Why and when can deep–but not shallow–networks avoid the curse of dimensionality T Poggio, H Mhaskar, L Rosasco, B Miranda, Q Liao Center for Brains, Minds and Machines (CBMM) Memo No. 58, arXiv preprint …, 2016 | 2 | 2016 |
| Sketching: a Cognitively inspired Compositional Theorem Prover that Learns to Prove-a Proposal B Miranda | 1 | 2020 |
| Establishing the foundations of Meta-learning-a Proposal B Miranda | 1 | 2020 |
| An empirical study of the properties of meta-learning-presentation B Miranda | 1 | 2020 |
| DiMO: Differentiable Model Optimization and metaDiMO B Miranda | 1 | 2019 |
TOMASO POGGIOMcDermott Professor in Brain Sciences, MITVerified email at ai.mit.edu
