Federico Errica

Federico Errica

Research Scientist

NEC Laboratories Europe

Biography

Federico Errica received his PhD in Computer Science from the University of Pisa, supervised by Alessio Micheli and Davide Bacciu. He is now a Research Scientist at NEC Laboratories Europe GmbH. His research interests include deep probabilistic models for graphs, neural networks and hybrid architectures.

Download: PhD thesis

Interests
  • Graph Machine Learning
  • Probabilistic Models
Education
  • PhD in Computer Science, 2018-2022

    University of Pisa

  • MSc in Computer Science, 2015-2018

    University of Pisa

  • BSc in Computer Science, 2012-2015

    University of Pisa

Skills

Technical
Pytorch
Deep Graph Networks
Keep experiments running
Hobbies
Painting
Cats
Videogames

Experience

 
 
 
 
 
NEC Laboratories Europe
Research Scientist
NEC Laboratories Europe
January 2022 – Present Heidelberg, DE
 
 
 
 
 
University College London
Visiting Researcher
University College London
January 2021 – May 2021 Remote
 
 
 
 
 
Facebook AI Research
Research Intern
Facebook AI Research
June 2019 – September 2019 London, UK
 
 
 
 
 
Laife Reply
Machine Learning Researcher
Laife Reply
March 2018 – September 2018 Milan, Italy

Projects

Notes on the Reparametrization Trick
One of the most popular articles of the last years in the Machine Learning community is the Auto-Encoding Variational Bayes, which also includes the so-called reparametrization trick. Intrigued by what was sketched in the article, I decided to work out the details of this reparametrization, covering 2 of the 3 cases described (but I guess the third one can be derived from the first 2).
Derivations of SNE and t-SNE
Recently I was looking at Stochastic Neigbhor Embedding (SNE) and its t-distributed version (t-SNE), but I could not find the exact steps to derive the gradient of the loss function (there are small errors in the t-SNE article and no info in the SNE one), so I decided to carry on the derivation and share it.

Contact