Search Results for author: Luca Cosmo

Found 23 papers, 13 papers with code

The Average Mixing Kernel Signature

1 code implementation ECCV 2020 Luca Cosmo, Giorgia Minello, Michael Bronstein, Luca Rossi, Andrea Torsello

We introduce the Average Mixing Kernel Signature (AMKS), a novel signature for points on non-rigid three-dimensional shapes based on the average mixing kernel and continuous-time quantum walks.


COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations

1 code implementation25 Apr 2024 Ruben Ciranni, Emilian Postolache, Giorgio Mariani, Michele Mancusi, Luca Cosmo, Emanuele Rodolà

We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples.

Contrastive Learning Music Generation

Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models

1 code implementation18 Mar 2024 Emilian Postolache, Giorgio Mariani, Luca Cosmo, Emmanouil Benetos, Emanuele Rodolà

Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation.

Graph Generation via Spectral Diffusion

no code implementations29 Feb 2024 Giorgia Minello, Alessandro Bicciato, Luca Rossi, Andrea Torsello, Luca Cosmo

In this paper, we present GRASP, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process.

Denoising Graph Generation

Multi-Source Diffusion Models for Simultaneous Music Generation and Separation

1 code implementation4 Feb 2023 Giorgio Mariani, Irene Tallini, Emilian Postolache, Michele Mancusi, Luca Cosmo, Emanuele Rodolà

In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score of the joint probability density of sources sharing a context.

Imputation Music Generation

Latent Autoregressive Source Separation

1 code implementation9 Jan 2023 Emilian Postolache, Giorgio Mariani, Michele Mancusi, Andrea Santilli, Luca Cosmo, Emanuele Rodolà

Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task performance.

Dimensionality Reduction

Spectral Maps for Learning on Subgraphs

no code implementations30 May 2022 Marco Pegoraro, Riccardo Marin, Arianna Rampini, Simone Melzi, Luca Cosmo, Emanuele Rodolà

We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.

Graph Learning Knowledge Distillation

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications

no code implementations1 Apr 2022 Kamilia Mullakaeva, Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael M. Bronstein

In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation.

Property Prediction

Graph Kernel Neural Networks

no code implementations14 Dec 2021 Luca Cosmo, Giorgia Minello, Michael Bronstein, Emanuele Rodolà, Luca Rossi, Andrea Torsello

The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter.

Graph Classification

Shape registration in the time of transformers

1 code implementation NeurIPS 2021 Giovanni Trappolini, Luca Cosmo, Luca Moschella, Riccardo Marin, Simone Melzi, Emanuele Rodolà

In this paper, we propose a transformer-based procedure for the efficient registration of non-rigid 3D point clouds.

Universal Spectral Adversarial Attacks for Deformable Shapes

no code implementations CVPR 2021 Arianna Rampini, Franco Pestarini, Luca Cosmo, Simone Melzi, Emanuele Rodolà

Our attacks are universal, in that they transfer across different shapes, different representations (meshes and point clouds), and generalize to previously unseen data.

Learning Spectral Unions of Partial Deformable 3D Shapes

1 code implementation31 Mar 2021 Luca Moschella, Simone Melzi, Luca Cosmo, Filippo Maggioli, Or Litany, Maks Ovsjanikov, Leonidas Guibas, Emanuele Rodolà

Spectral geometric methods have brought revolutionary changes to the field of geometry processing.

Learning disentangled representations via product manifold projection

no code implementations2 Mar 2021 Marco Fumero, Luca Cosmo, Simone Melzi, Emanuele Rodolà

We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations.


Latent-Graph Learning for Disease Prediction

no code implementations27 Mar 2020 Luca Cosmo, Anees Kazi, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful machine learning tool for Computer-Aided Diagnosis (CADx) and disease prediction.

Disease Prediction General Classification +1

LIMP: Learning Latent Shape Representations with Metric Preservation Priors

1 code implementation ECCV 2020 Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà

In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes.

Style Transfer

Differentiable Graph Module (DGM) for Graph Convolutional Networks

1 code implementation11 Feb 2020 Anees Kazi, Luca Cosmo, Seyed-Ahmad Ahmadi, Nassir Navab, Michael Bronstein

We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning).

Disease Prediction Graph Neural Network +2

Isospectralization, or how to hear shape, style, and correspondence

1 code implementation CVPR 2019 Luca Cosmo, Mikhail Panine, Arianna Rampini, Maks Ovsjanikov, Michael M. Bronstein, Emanuele Rodolà

The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.

Style Transfer

Parameter-Free Lens Distortion Calibration of Central Cameras

no code implementations ICCV 2017 Filippo Bergamasco, Luca Cosmo, Andrea Gasparetto, Andrea Albarelli, Andrea Torsello

At the core of many Computer Vision applications stands the need to define a mathematical model describing the imaging process.

Partial Functional Correspondence

1 code implementation17 Jun 2015 Emanuele Rodolà, Luca Cosmo, Michael M. Bronstein, Andrea Torsello, Daniel Cremers

In this paper, we propose a method for computing partial functional correspondence between non-rigid shapes.

Adopting an Unconstrained Ray Model in Light-Field Cameras for 3D Shape Reconstruction

no code implementations CVPR 2015 Filippo Bergamasco, Andrea Albarelli, Luca Cosmo, Andrea Torsello, Emanuele Rodola, Daniel Cremers

This results in several drawbacks, ranging from the difficulties in feature detection, due to the reduced size of each microlens, to the need to adopt a model with a relatively small number of parameters.

3D Reconstruction 3D Shape Reconstruction

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