Search Results for author: Federico Monti

Found 17 papers, 10 papers with code

Learning to Infer Structures of Network Games

no code implementations16 Jun 2022 Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong

We adopt a transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function.

Learning to Infer the Structure of Network Games

no code implementations29 Sep 2021 Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong

Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbors.

Temporal Graph Networks for Deep Learning on Dynamic Graphs

5 code implementations18 Jun 2020 Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, Michael Bronstein

Graph Neural Networks (GNNs) have recently become increasingly popular due to their ability to learn complex systems of relations or interactions arising in a broad spectrum of problems ranging from biology and particle physics to social networks and recommendation systems.

Recommendation Systems

SIGN: Scalable Inception Graph Neural Networks

3 code implementations23 Apr 2020 Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, Federico Monti

Graph representation learning has recently been applied to a broad spectrum of problems ranging from computer graphics and chemistry to high energy physics and social media.

Graph Representation Learning Graph Sampling +1

ncRNA Classification with Graph Convolutional Networks

1 code implementation16 May 2019 Emanuele Rossi, Federico Monti, Michael Bronstein, Pietro Liò

Non-coding RNA (ncRNA) are RNA sequences which don't code for a gene but instead carry important biological functions.

Classification General Classification

Fake News Detection on Social Media using Geometric Deep Learning

3 code implementations10 Feb 2019 Federico Monti, Fabrizio Frasca, Davide Eynard, Damon Mannion, Michael M. Bronstein

One of the main reasons is that often the interpretation of the news requires the knowledge of political or social context or 'common sense', which current NLP algorithms are still missing.

Common Sense Reasoning Fact Checking +2

Using Attribution to Decode Dataset Bias in Neural Network Models for Chemistry

no code implementations27 Nov 2018 Kevin McCloskey, Ankur Taly, Federico Monti, Michael P. Brenner, Lucy Colwell

The dataset bias makes these models unreliable for accurately revealing information about the mechanisms of protein-ligand binding.

Graph Neural Networks for IceCube Signal Classification

1 code implementation17 Sep 2018 Nicholas Choma, Federico Monti, Lisa Gerhardt, Tomasz Palczewski, Zahra Ronaghi, Prabhat, Wahid Bhimji, Michael M. Bronstein, Spencer R. Klein, Joan Bruna

Tasks involving the analysis of geometric (graph- and manifold-structured) data have recently gained prominence in the machine learning community, giving birth to a rapidly developing field of geometric deep learning.

Classification General Classification

Dual-Primal Graph Convolutional Networks

no code implementations3 Jun 2018 Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski, Or Litany, Stephan Günnemann, Michael M. Bronstein

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs.

Graph Attention Recommendation Systems

MotifNet: a motif-based Graph Convolutional Network for directed graphs

no code implementations4 Feb 2018 Federico Monti, Karl Otness, Michael M. Bronstein

Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community.

BIG-bench Machine Learning

CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS

no code implementations ICLR 2018 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

Community Detection General Classification +2

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

2 code implementations22 May 2017 Ron Levie, Federico Monti, Xavier Bresson, Michael M. Bronstein

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains.

Community Detection General Classification +3

Generative Convolutional Networks for Latent Fingerprint Reconstruction

no code implementations4 May 2017 Jan Svoboda, Federico Monti, Michael M. Bronstein

Performance of fingerprint recognition depends heavily on the extraction of minutiae points.

Geometric deep learning on graphs and manifolds using mixture model CNNs

4 code implementations CVPR 2017 Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.

Document Classification Graph Classification +7

Deep convolutional neural networks for pedestrian detection

1 code implementation13 Oct 2015 Denis Tomè, Federico Monti, Luca Baroffio, Luca Bondi, Marco Tagliasacchi, Stefano Tubaro

Pedestrian detection is a popular research topic due to its paramount importance for a number of applications, especially in the fields of automotive, surveillance and robotics.

Image Classification object-detection +3

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