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Giorgio Giannone

Technical University of Denmark (DTU)

7 papers hereh-index 10503 citations23 works total

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

author position
  • first author5
  • middle author1

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV2
  • cs.AI1
affiliations
  • Technical University of Denmark (DTU)
  • Massachusetts Institute of Technology (MIT)
Homepage
same name
  • Giorgio Giannone — 2 papers
  • Giorgio Giannone — 2 papers
  • Giorgio Giannone — 1 paper

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
20182025
most citedFew-Shot Diffusion Models

20 citations · 28 across the 4 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling

Giorgio Giannone, Guangxuan Xu, Nikhil Shivakumar Nayak +4

Inference-Time Scaling (ITS) improves language models by allocating more computation at generation time. Particle Filtering (PF) has emerged as a strong ITS method for complex math…

cs.LG2024

Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling

Harry Jake Cunningham, Giorgio Giannone, Mingtian Zhang +1

Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel parameterizations must…

cs.LG2022

Just Mix Once: Worst-group Generalization by Group Interpolation

Giorgio Giannone, Serhii Havrylov, Jordan Massiah +2

Advances in deep learning theory have revealed how average generalization relies on superficial patterns in data. The consequences are brittle models with poor performance with shi…

cs.LG2019

No Representation without Transformation

Giorgio Giannone, Saeed Saremi, Jonathan Masci +1

We extend the framework of variational autoencoders to represent transformations explicitly in the latent space. In the family of hierarchical graphical models that emerges, the la…

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