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F. Bianchi

55 papers hereh-index 324.2k citations93 works total

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

author position
  • sole author1
  • first author19
  • middle author26
  • last author9

Across the 55 of 55 papers where every author was matched, so the position is known.

fields
  • cs.LG23
  • cs.NE9
  • stat.ML7
  • cs.CV6
  • physics.data-an3
  • cs.CY1
same name
  • F. Bianchi — 425 papers
  • F. Bianchi — 286 papers, h 0
  • F. Bianchi — 233 papers, h 38
  • F. Bianchi — 128 papers, h 12
  • F. Bianchi — 100 papers, h 3
  • F. Bianchi — 88 papers, h 6

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
20142026
most citedGraph Neural Networks with convolutional ARMA filters

439 citations · 2k across the 50 of their papers we have counts for

collaborators
Showing physics.data-anShow all

3 papers · 1 filter

physics.data-an2016★ 67 cited

Determination of the edge of criticality in echo state networks through Fisher information maximization

Lorenzo Livi, Filippo Maria Bianchi, Cesare Alippi

It is a widely accepted fact that the computational capability of recurrent neural networks is maximized on the so-called "edge of criticality". Once the network operates in this c…

physics.data-an2016★ 101 cited

Investigating echo state networks dynamics by means of recurrence analysis

Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi

In this paper, we elaborate over the well-known interpretability issue in echo state networks. The idea is to investigate the dynamics of reservoir neurons with time-series analysi…

physics.data-an2015★ 25 cited

Data-driven detrending of nonstationary fractal time series with echo state networks

Enrico Maiorino, Filippo Maria Bianchi, Lorenzo Livi +2

In this paper, we propose a novel data-driven approach for removing trends (detrending) from nonstationary, fractal and multifractal time series. We consider real-valued time serie…

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