Edoardo Vecchi
9 Papers
1 Citations
Edoardo Vecchi is an academic researcher. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 5 publications.
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Papers
eSPA+: Scalable Entropy-Optimal Machine Learning Classification for Small Data Problems
TL;DR: This work proposes eSPA+, a significant extension of the recently formulated entropy-optimal scalable probabilistic approximation algorithm, and proves that with these two enhancements, eSPA+ moves from the polynomial to the linear class of complexity scaling algorithms.
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On cheap entropy-sparsified regression learning
Illia Horenko,Edoardo Vecchi,Juraj Kardos,Andreas Wächter,Olaf Schenk,Terence J. O’Kane,Patrick Gagliardini,Susanne Gerber +7 more
TL;DR: SPARTAn as discussed by the authors decomposes a very general setting of regression learning into an iterative sequence of simple substeps, which are either analytically solvable or cheaply computable through an efficient second-order numerical solver with a sublinear cost scaling.
14
Low-Cost Probabilistic 3D Denoising with Applications for Ultra-Low-Radiation Computed Tomography
Illia Horenko,L. Pospisil,Edoardo Vecchi,Steffen Albrecht,Alexander Gerber,Beate Rehbock,Albrecht Stroh,Susanne Gerber +7 more
TL;DR: A parallel Probabilistic Mumford–Shah denoising model (PMS) is introduced and it is shown that it markedly-outperforms the compared common Denoising methods in denoizing quality and cost scaling.
Investigating the Efficacy and Interpretability of ML Classifiers for Student Performance Prediction in the Small-Data Regime
TL;DR: This study systematically benchmarks ML algorithms for student performance prediction in the small-data regime, demonstrating robust predictive performance and interpretability of models, even with class imbalance, and highlighting their adaptability in uncovering alternative predictive patterns.
Spiking neural networks provide accurate, efficient and robust models for whisker stimulus classification and allow for inter-individual generalization
Steffen Albrecht,Jens R Vandevelde,Edoardo Vecchi,Davide Bassetti,Maik C. Stüttgen,Heiko J. Luhmann,Illia Horenko +6 more
TL;DR: In this article , the authors benchmarked a selection of machine learning classification algorithms on the tasks of whisker stimulus detection, stimulus classification and behavior prediction based on electrophysiological recordings of layer-resolved local field potentials from the barrel cortex of awake mice.