27 citations · 47 across the 10 of their papers we have counts for
11 papers
Flow Matching for Fluctuation-Based Fluorescence Super-Resolution
Alberto Cozzani, Giacomo Meanti, Laure Blanc-Féraud +1
Fluctuation-based super-resolution techniques are an appealing alternative to single-molecule and structured-illumination microscopy, as they adapt to standard acquisition settings…
PIKS: Universal Physics-Informed Kernel Methods
Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1
Physics-informed machine learning incorporates physical principles --often expressed via differential operators-- into data-driven models. While physics-informed neural networks (P…
kooplearn: A Scikit-Learn Compatible Library of Algorithms for Evolution Operator Learning
Giacomo Turri, Grégoire Pacreau, Giacomo Meanti +8
kooplearn is a machine-learning library that implements linear, kernel, and deep-learning estimators of dynamical operators and their spectral decompositions. kooplearn can model b…
Fast and Fourier Features for Transfer Learning of Interatomic Potentials
Pietro Novelli, Giacomo Meanti, Pedro J. Buigues +4
Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To thi…
Estimating Koopman operators with sketching to provably learn large scale dynamical systems
Giacomo Meanti, Antoine Chatalic, Vladimir R. Kostic +3
The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems. Estimators such as principal component…
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
Sara Fridovich-Keil, Giacomo Meanti, Frederik Warburg +2
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses d choose 2 planes to represent a d-dimensional scene, providing a seamless way…
Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
Federico Ceola, Elisa Maiettini, Giulia Pasquale +3
The visual system of a robot has different requirements depending on the application: it may require high accuracy or reliability, be constrained by limited resources or need fast…
Physics Informed Shallow Machine Learning for Wind Speed Prediction
Daniele Lagomarsino-Oneto, Giacomo Meanti, Nicolò Pagliana +4
The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the…
Multiclass learning with margin: exponential rates with no bias-variance trade-off
Stefano Vigogna, Giacomo Meanti, Ernesto De Vito +1
We study the behavior of error bounds for multiclass classification under suitable margin conditions. For a wide variety of methods we prove that the classification error under a h…
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Giacomo Meanti, Luigi Carratino, Ernesto De Vito +1
Kernel methods provide a principled approach to nonparametric learning. While their basic implementations scale poorly to large problems, recent advances showed that approximate so…
Kernel methods through the roof: handling billions of points efficiently
Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco +1
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems, since naïve implementations scale poor…