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Randall Balestriero
Randall Balestriero
AI Researcher
Verified email at brown.edu - Homepage
Title
Cited by
Cited by
Year
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
L Seydoux, R Balestriero, P Poli, M Hoop, M Campillo, R Baraniuk
Nature communications 11 (1), 3972, 2020
1602020
A spline theory of deep learning
R Balestriero, R Baraniuk
International Conference on Machine Learning, 374-383, 2018
1292018
Learning in high dimension always amounts to extrapolation
R Balestriero, J Pesenti, Y LeCun
arXiv preprint arXiv:2110.09485, 2021
1202021
Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods
R Balestriero, Y LeCun
Advances in Neural Information Processing Systems 35, 26671-26685, 2022
1162022
Mad max: Affine spline insights into deep learning
R Balestriero, RG Baraniuk
Proceedings of the IEEE, 1-24, 2020
992020
The effects of regularization and data augmentation are class dependent
R Balestriero, L Bottou, Y LeCun
Advances in Neural Information Processing Systems 35, 37878-37891, 2022
822022
The recurrent neural tangent kernel
S Alemohammad, Z Wang, R Balestriero, R Baraniuk
International Conference on Learning Representations, 2020
782020
The geometry of deep networks: Power diagram subdivision
R Balestriero, R Cosentino, B Aazhang, R Baraniuk
Advances in Neural Information Processing Systems 32, 15832--15841, 2019
582019
Neural decision trees
R Balestriero
arXiv preprint arXiv:1702.07360, 2017
552017
Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, and Micah Goldblum
R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ...
A cookbook of self-supervised learning 2, 2023
542023
Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank
Q Garrido, R Balestriero, L Najman, Y Lecun
International conference on machine learning, 10929-10974, 2023
502023
High fidelity visualization of what your self-supervised representation knows about
F Bordes, R Balestriero, P Vincent
arXiv preprint arXiv:2112.09164, 2021
462021
Spline filters for end-to-end deep learning
R Balestriero, R Cosentino, H Glotin, R Baraniuk
International conference on machine learning, 364-373, 2018
352018
Imagenet-x: Understanding model mistakes with factor of variation annotations
BY Idrissi, D Bouchacourt, R Balestriero, I Evtimov, C Hazirbas, N Ballas, ...
arXiv preprint arXiv:2211.01866, 2022
342022
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular Values
A Imtiaz Humayun, R Balestriero, R Baraniuk
arXiv e-prints, arXiv: 2203.01993, 2022
34*2022
Guillotine regularization: Why removing layers is needed to improve generalization in self-supervised learning
F Bordes, R Balestriero, Q Garrido, A Bardes, P Vincent
arXiv preprint arXiv:2206.13378, 2022
33*2022
The hidden uniform cluster prior in self-supervised learning
M Assran, R Balestriero, Q Duval, F Bordes, I Misra, P Bojanowski, ...
arXiv preprint arXiv:2210.07277, 2022
322022
A data-augmentation is worth a thousand samples: Analytical moments and sampling-free training
R Balestriero, I Misra, Y LeCun
Advances in Neural Information Processing Systems 35, 19631-19644, 2022
28*2022
MaGNET: Uniform sampling from deep generative network manifolds without retraining
AI Humayun, R Balestriero, R Baraniuk
arXiv preprint arXiv:2110.08009, 2021
272021
Implicit rugosity regularization via data augmentation
D LeJeune, R Balestriero, H Javadi, RG Baraniuk
arXiv preprint arXiv:1905.11639, 2019
23*2019
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Articles 1–20