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Giovanni Paolini

Amazon Web Services

24 papers hereh-index 9698 citations23 works total

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

author position
  • sole author7
  • first author3
  • middle author9
  • last author5

Across the 24 of 24 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • math.AT4
  • math.GR4
  • math.CO3
  • cs.CL2
  • cs.CG1
affiliations
  • Amazon Web Services
  • California Institute of Technology
HomepageORCID 0000-0002-3964-9101
same name
  • Giovanni Paolini — 4 papers, h 4
  • Giovanni Paolini — 4 papers, h 0
  • Giovanni Paolini — 2 papers, h 1

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 citedWhere is the Information in a Deep Neural Network?

46 citations · 160 across the 23 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

math.CO2019★ 7 cited

Representations of torsion-free arithmetic matroids

Roberto Pagaria, Giovanni Paolini

We study the representability problem for torsion-free arithmetic matroids. By using a new operation called "reduction" and a "signed Hermite normal form", we provide and implement…

math.GR2019★ 32 cited

Proof of the K(π,1) conjecture for affine Artin groups

Giovanni Paolini, Mario Salvetti

We prove the K(π,1) conjecture for affine Artin groups: the complexified complement of an affine reflection arrangement is a classifying space. This is a long-standing problem, d…

cs.LG2019★ 46 cited

Where is the Information in a Deep Neural Network?

Alessandro Achille, Giovanni Paolini, Stefano Soatto

Whatever information a deep neural network has gleaned from training data is encoded in its weights. How this information affects the response of the network to future data remains…

cs.LG2019

The Information Complexity of Learning Tasks, their Structure and their Distance

Alessandro Achille, Giovanni Paolini, Glen Mbeng +1

We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational for the practice of transfer learni…

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