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Simone Scardapane

62 papers hereh-index 336k citations146 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author2
  • first author14
  • middle author32
  • last author10

Across the 58 of 62 papers where every author was matched, so the position is known.

fields
  • cs.LG29
  • stat.ML11
  • cs.NE7
  • cs.CV5
  • eess.SP4
  • eess.AS2
same name
  • Simone Scardapane — 5 papers, h 3
  • Simone Scardapane — 2 papers, h 2
  • Simone Scardapane — 2 papers, h 2
  • Simone Scardapane — 1 paper, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162024
most citedGroup Sparse Regularization for Deep Neural Networks

477 citations · 1.4k across the 57 of their papers we have counts for

collaborators
Showing 2016 · stat.MLShow all

4 papers · 2 filters

stat.ML2016★ 25 cited

A Framework for Parallel and Distributed Training of Neural Networks

Simone Scardapane, Paolo Di Lorenzo

The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where training data is partitioned over a set of agents tha…

stat.ML2016

Distributed Supervised Learning using Neural Networks

Simone Scardapane

Distributed learning is the problem of inferring a function in the case where training data is distributed among multiple geographically separated sources. Particularly, the focus…

stat.ML2016★ 477 cited

Group Sparse Regularization for Deep Neural Networks

Simone Scardapane, Danilo Comminiello, Amir Hussain +1

In this paper, we consider the joint task of simultaneously optimizing (i) the weights of a deep neural network, (ii) the number of neurons for each hidden layer, and (iii) the sub…

stat.ML2016★ 8 cited

Learning activation functions from data using cubic spline interpolation

Simone Scardapane, Michele Scarpiniti, Danilo Comminiello +1

Neural networks require a careful design in order to perform properly on a given task. In particular, selecting a good activation function (possibly in a data-dependent fashion) is…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.