Research
I am broadly interested in Probabilistic Machine Learning, Geometry and Perception,
with a focus on Bayesian Statistics, Variational Inference and Deep Generative Modelling.
Curious about Probabilistic Agents and Causal Inference.

Publications


Transformationaware Variational Autoencoder
Giorgio Giannone,
Saeed Saremi,
Jonathan Masci,
Christian Osendorfer
Underreview, 2020
We extend the framework of variational autoencoders to represent transformations
explicitly in the latent space. This is achieved in the form of a generative model
structured such that the group of transformations
that act in the input space is instead represented by latent variables
which are linear operators that only act in the latent space.
In the family of hierarchical graphical models that emerges,
the latent space is populated by higher order objects which are inferred
jointly with the latent representations they act on.


Realtime Classification from Short EventCamera Streams using Inputfiltering Neural ODEs
Giorgio Giannone,
Asha Anoosheh,
Alessio Quaglino,
Pierluca D'Oro,
Marco Gallieri,
Jonathan Masci
Interpretable Inductive Biases
and Physically Structured Learning Workshop, NeurIPS, 2020
Eventbased cameras are novel, efficient sensors inspired by the human vision system,
generating an asynchronous, pixelwise stream of data.
Learning from such data is generally performed through heavy preprocessing and event integration into images.
This requires buffering of possibly long sequences and can limit the response time of the inference system.
In this work, we instead propose to directly use events from a DVS camera,
a stream of intensity changes and their spatial coordinates.
This sequence is used as the input for a novel asynchronous RNNlike architecture,
the Inputfiltering Neural ODEs.


No Representation without Transformation
Giorgio Giannone,
Jonathan Masci,
Christian Osendorfer
Bayesian Deep Learning and Perception as Generative Reasoning Workshops, NeurIPS , 2019
We propose to extend Latent Variable Models with a simple idea:
learn to encode not only samples but also transformations of such samples.
This means that the latent space is not only populated by embeddings
but also by higher order objects that map between these embeddings.
We show how a hierarchical graphical model can be utilized to enforce
desirable algebraic properties of such latent mappings.


Learning Common Representation from RGB and Depth Images
Giorgio Giannone,
Boris Chidlovskii
Multimodal Learning and Applications Workshop, CVPR, 2019
We propose a new deep learning architecture for the tasks of semantic segmentation
and depth prediction from RGBD images.
We revise the state of art based on the RGB and depth feature fusion,
where both modalities are assumed to be available at train and test time.
We propose a new architecture where the feature fusion is replaced with a common deep representation.
Combined with an encoderdecoder type of the network, the architecture can jointly learn models
for semantic segmentation and depth estimation based on their common representation.

Theses


Learning Common Representation for Scene Understanding
Giorgio Giannone
Master's Thesis, Data Science, Sapienza University of Rome, 2018


Bubble Dynamics in Turbulent Shear Flows
Giorgio Giannone
Master's Thesis, Mechanical Engineering, Sapienza University of Rome, 2016

