| The geometry of deep networks: Power diagram subdivision R Balestriero, R Cosentino, B Aazhang, R Baraniuk Advances in neural information processing systems., 2019 | 52 | 2019 |
| Spline filters for end-to-end deep learning R Balestriero*, R Cosentino*, H Glotin, R Baraniuk (*equal contribution) International Conference on Machine Learning, 364-373, 2018 | 33 | 2018 |
| Learnable group transform for time-series R Cosentino, B Aazhang International conference on machine learning, 2164-2173, 2020 | 17 | 2020 |
| Toward a geometrical understanding of self-supervised contrastive learning R Cosentino, A Sengupta, S Avestimehr, M Soltanolkotabi, A Ortega, ... arXiv preprint arXiv:2205.06926, 2022 | 15 | 2022 |
| Deep Autoencoders: From Understanding to Generalization Guarantees R Cosentino, R Balestriero, R Baraniuk, B Aazhang Mathematical and Scientific Machine Learning 2021, 2021 | 9* | 2021 |
| Best basis selection using sparsity driven multi-family wavelet transform R Cosentino, R Balestriero, B Aazhang 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP …, 2016 | 9 | 2016 |
| The geometry of self-supervised learning models and its impact on transfer learning R Cosentino*, S Shekkizhar*, M Soltanolkotabi, S Avestimehr, ... arXiv preprint arXiv:2209.08622, 2022 | 8 | 2022 |
| Overcomplete frame thresholding for acoustic scene analysis R Cosentino, R Balestriero, R Baraniuk, A Patel arXiv preprint arXiv:1712.09117, 2017 | 4 | 2017 |
| Spatial Transformer K-Means R Cosentino, R Balestriero, Y Bahroun, A Sengupta, R Baraniuk, ... 2022 56th Asilomar Conference on Signals, Systems, and Computers, 1444-1448, 2022 | 3 | 2022 |
| A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa A Banta, R Cosentino, MM John, A Post, S Buchan, M Razavi, B Aazhang Artificial intelligence in medicine 118, 102135, 2021 | 3 | 2021 |
| Universal frame thresholding R Cosentino, R Balestriero, RG Baraniuk, B Aazhang IEEE Signal Processing Letters 27, 1115-1119, 2020 | 3 | 2020 |
| Characterizing Large Language Model Geometry Solves Toxicity Detection and Generation R Balestriero*, R Cosentino*, S Shekkizhar* (* equal contrib.) arXiv preprint arXiv:2312.01648, 2023 | 2 | 2023 |
| Nonlinear Regression with a Convolutional Encoder-Decoder for Remote Monitoring of Surface Electrocardiograms A Banta, R Cosentino, MM John, A Post, S Buchan, M Razavi, B Aazhang arXiv preprint arXiv:2012.06003, 2020 | 2 | 2020 |
| Manifold Approximating Graph Interpolation of Cardiac Local Activation Time J Hellar, R Cosentino, MM John, A Post, S Buchan, M Razavi, B Aazhang IEEE Transactions on Biomedical Engineering 69 (10), 3253-3264, 2022 | 1 | 2022 |
| Graph-based interpolation of local activation time on the cardiac surface J Hellar, R Cosentino, MM John, A Post, S Buchan, M Razavi, B Aazhang 2021 55th Asilomar Conference on Signals, Systems, and Computers, 274-278, 2021 | 1 | 2021 |
| B-PO04-063 THE GHOSTS OF ARRHYTHMIAS PAST: RECONSTRUCTING 12-LEAD ECG FROM INTRACARDIAC ELECTROGRAMS A Banta, RF Cosentino, MM John, A Post, S Buchan, M Razavi, ... Heart Rhythm 18 (8), S304, 2021 | | 2021 |
| How to Group: From Time Series to Manifold R Cosentino-Faugere Rice University, 2021 | | 2021 |
| Interpretable Image Clustering via Diffeomorphism-Aware K-Means R Cosentino, R Balestriero, Y Bahroun, A Sengupta, R Baraniuk, ... arXiv preprint arXiv:2012.09743, 2020 | | 2020 |
| Sparse Multi-Family Deep Scattering Network R Cosentino, R Balestriero arXiv preprint arXiv:2012.07662, 2020 | | 2020 |
| Interpretable Image Clustering via Diffeomorphism-Aware K-Means R Cosentino, R Balestriero, Y Bahroun, A Sengupta, R Baraniuk, ... arXiv preprint arXiv:2012.09743, 2020 | | 2020 |
Randall BalestrieroAI ResearcherDirección de correo verificada de citadel.com
