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Brando Miranda
Brando Miranda
MIT, UIUC, Stanford
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
6662017
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), 4405, 2018
2332018
Are emergent abilities of large language models a mirage?
R Schaeffer, B Miranda, S Koyejo
Advances in Neural Information Processing Systems 36, 2024
1432024
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
1262017
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
115*2018
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
352018
Theory IIIb: Generalization in deep networks
T Poggio, Q Liao, B Miranda, A Banburski, X Boix, J Hidary
arXiv preprint arXiv:1806.11379, 2018
312018
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 73, 1-38, 2018
252018
Theory III: Dynamics and generalization in deep networks
A Banburski, Q Liao, B Miranda, L Rosasco, F De La Torre, J Hidary, ...
arXiv preprint arXiv:1903.04991, 2019
142019
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, https://arxiv …, 2019
142019
Theory I: Why and when can deepbut 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
142016
Beyond scale: the diversity coefficient as a data quality metric demonstrates llms are pre-trained on formally diverse data
A Lee, B Miranda, S Koyejo
arXiv preprint arXiv:2306.13840, 2023
112023
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
92018
High-resolution on-chip digital Fourier transform spectroscopy
DM Kita, B Mirandat, D Favelai, D Bono, J Michon, H Lin, T Gu, J Hu
2018 Conference on Lasers and Electro-Optics (CLEO), 1-2, 2018
52018
Sketching: a Cognitively inspired Compositional Theorem Prover that Learns to Prove-a Proposal
B Miranda
42020
The curse of low task diversity: On the failure of transfer learning to outperform maml and their empirical equivalence
B Miranda, P Yu, YX Wang, S Koyejo
arXiv preprint arXiv:2208.01545, 2022
32022
An empirical study of the properties of meta-learning-presentation
B Miranda
32020
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
32018
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
32016
Does MAML Only Work via Feature Re-use? A Data Centric Perspective
B Miranda, YX Wang, S Koyejo
arXiv preprint arXiv:2112.13137, 2021
22021
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