| Infinitely deep neural networks as diffusion processes S Peluchetti, S Favaro International Conference on Artificial Intelligence and Statistics (AISTAT …, 2020 | 42 | 2020 |
| Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling S Peluchetti Journal of Machine Learning Research (JMLR) 24 (374), 1−51, 2023 | 36 | 2023 |
| Stable behaviour of infinitely wide deep neural networks S Peluchetti, S Favaro, S Fortini International Conference on Artificial Intelligence and Statistics (AISTAT …, 2020 | 33 | 2020 |
| -Strong simulation of the Brownian path A Beskos, S Peluchetti, G Roberts Bernoulli 18 (4), 1223-1248, 2012 | 24 | 2012 |
| Large-width functional asymptotics for deep Gaussian neural networks D Bracale, S Favaro, S Fortini, S Peluchetti International Conference on Learning Representations (ICLR), 2021 | 20 | 2021 |
| Non-Denoising Forward-Time Diffusions S Peluchetti | 19 | 2021 |
| Deep Stable neural networks: large-width asymptotics and convergence rates S Favaro, S Fortini, S Peluchetti Bernoulli 29 (3), 2574-2597, 2023 | 13 | 2023 |
| Learning Stochastic Optimal Policies via Gradient Descent S Massaroli, M Poli, S Peluchetti, J Park, A Yamashita, H Asama IEEE Control Systems Letters 6, 1094-1099, 2022 | 12 | 2022 |
| A Bayesian nonparametric approach to count-min sketch under power-law data streams E Dolera, S Favaro, S Peluchetti International Conference on Artificial Intelligence and Statistics (AISTAT …, 2021 | 10 | 2021 |
| Large-width asymptotics for ReLU neural networks with α-Stable initializations S Favaro, S Fortini, S Peluchetti arXiv preprint arXiv:2206.08065, 2023 | 8* | 2023 |
| Doubly infinite residual neural networks: a diffusion process approach S Peluchetti, S Favaro Journal of Machine Learning Research (JMLR) 22 (175), 1-48, 2021 | 7* | 2021 |
| An empirical study of pretrained representations for few-shot classification T Ramalho, T Sousbie, S Peluchetti Neural Information Processing Systems (NeurIPS) Workshop on Meta-Learning, 2019 | 7 | 2019 |
| Learning-augmented count-min sketches via Bayesian nonparametrics D Emanuele, S Favaro, P Stefano Journal of Machine Learning Research (JMLR) 24 (12), 1-60, 2023 | 6* | 2023 |
| An empirical study of the efficiency of EA for diffusion simulation S Peluchetti, GO Roberts CRiSM Technical Report 2008 (08-14), 2008 | 5 | 2008 |
| An analysis of the efficiency of the Exact Algorithm S Peluchetti Bocconi University, 2008 | 5 | 2008 |
| Infinite-channel deep convolutional Stable neural networks D Bracale, S Favaro, S Fortini, S Peluchetti Neural Information Processing Systems (NeurIPS) Workshop on Bayesian Deep …, 2021 | 4* | 2021 |
| A Study of the Efficiency of Exact Methods for Diffusion Simulation S Peluchetti, GO Roberts Monte Carlo and Quasi-Monte Carlo Methods 2010, 161-187, 2012 | 4 | 2012 |
| Neural SDE - Information propagation through the lens of diffusion processes S Peluchetti, S Favaro Neural Information Processing Systems (NeurIPS) Workshop on Bayesian Deep …, 2019 | 3* | 2019 |
| BM: Coupled Schr\"{o}dinger Bridge Matching S Peluchetti arXiv preprint arXiv:2409.09376, 2024 | 1 | 2024 |
| Function-Space MCMC for Bayesian Wide Neural Networks L Pezzetti, S Favaro, S Peluchetti arXiv preprint arXiv:2408.14325, 2024 | | 2024 |
