Jary Pomponi

TL;DR: In this article, the authors propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch, which is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.

TL;DR: This work proposes a new, relatively simple and efficient method to perform continual learning by regularizing instead the network internal embeddings, and shows that this method performs favorably with respect to state-of-the-art approaches in the literature, while requiring significantly less space in memory and computational time.

TL;DR: In this paper, the authors propose a variant of the variational inference, where they replace the Kullback-Leibler divergence in the ELBO term with a Maximum Mean Discrepancy (MMD) estimator.

TL;DR: DeepRICH as mentioned in this paper is a deep learning algorithm for fast reconstruction which can be applied to different imaging Cherenkov detectors and leverages on a custom VAE combined to Maximum Mean Discrepancy (MMD), with a CNN extracting features from the space of the latent variables for classification.

TL;DR: DeepRICH is a novel deep learning algorithm for fast reconstruction which can be applied to different imaging Cherenkov detectors and has the advantage to bypass low-level details needed to build a likelihood, allowing for a sensitive improvement in computation time at potentially the same reconstruction performance of other established reconstruction algorithms.