Search Results for author: Alessio Micheli

Found 23 papers, 9 papers with code

Dynamic Graph Echo State Networks

1 code implementation16 Oct 2021 Domenico Tortorella, Alessio Micheli

Dynamic temporal graphs represent evolving relations between entities, e. g. interactions between social network users or infection spreading.

Phase Transition Adaptation

1 code implementation20 Apr 2021 Claudio Gallicchio, Alessio Micheli, Luca Silvestri

Artificial Recurrent Neural Networks are a powerful information processing abstraction, and Reservoir Computing provides an efficient strategy to build robust implementations by projecting external inputs into high dimensional dynamical system trajectories.

Pyramidal Reservoir Graph Neural Network

no code implementations10 Apr 2021 Filippo Maria Bianchi, Claudio Gallicchio, Alessio Micheli

We propose a deep Graph Neural Network (GNN) model that alternates two types of layers.

Graph Embedding

Graph Mixture Density Networks

1 code implementation5 Dec 2020 Federico Errica, Davide Bacciu, Alessio Micheli

We introduce the Graph Mixture Density Networks, a new family of machine learning models that can fit multimodal output distributions conditioned on graphs of arbitrary topology.

Density Estimation Graph Representation Learning

Ring Reservoir Neural Networks for Graphs

no code implementations11 May 2020 Claudio Gallicchio, Alessio Micheli

Machine Learning for graphs is nowadays a research topic of consolidated relevance.

Graph Classification

Machine learning approaches for identifying prey handling activity in otariid pinnipeds

no code implementations10 Feb 2020 Rita Pucci, Alessio Micheli, Stefano Chessa, Jane Hunter

Systems developed in wearable devices with sensors onboard are widely used to collect data of humans and animals activities with the perspective of an on-board automatic classification of data.

Edge-based sequential graph generation with recurrent neural networks

1 code implementation31 Jan 2020 Davide Bacciu, Alessio Micheli, Marco Podda

Graph generation with Machine Learning is an open problem with applications in various research fields.

Graph Generation

Theoretically Expressive and Edge-aware Graph Learning

no code implementations24 Jan 2020 Federico Errica, Davide Bacciu, Alessio Micheli

We propose a new Graph Neural Network that combines recent advancements in the field.

Graph Learning

A Gentle Introduction to Deep Learning for Graphs

2 code implementations29 Dec 2019 Davide Bacciu, Federico Errica, Alessio Micheli, Marco Podda

The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community.

Graph Representation Learning

A Fair Comparison of Graph Neural Networks for Graph Classification

1 code implementation ICLR 2020 Federico Errica, Marco Podda, Davide Bacciu, Alessio Micheli

We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

General Classification Graph Classification +2

Fast and Deep Graph Neural Networks

no code implementations20 Nov 2019 Claudio Gallicchio, Alessio Micheli

We address the efficiency issue for the construction of a deep graph neural network (GNN).

Reservoir Topology in Deep Echo State Networks

no code implementations24 Sep 2019 Claudio Gallicchio, Alessio Micheli

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning.

Embeddings and Representation Learning for Structured Data

no code implementations15 May 2019 Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Alessandro Sperduti

Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form.

Metric Learning Representation Learning

Richness of Deep Echo State Network Dynamics

no code implementations12 Mar 2019 Claudio Gallicchio, Alessio Micheli

Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs).

Tree Edit Distance Learning via Adaptive Symbol Embeddings

no code implementations ICML 2018 Benjamin Paaßen, Claudio Gallicchio, Alessio Micheli, Barbara Hammer

Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart.

Metric Learning

Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing

1 code implementation ICML 2018 Davide Bacciu, Federico Errica, Alessio Micheli

We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data.

General Classification

Deep Echo State Networks for Diagnosis of Parkinson's Disease

no code implementations19 Feb 2018 Claudio Gallicchio, Alessio Micheli, Luca Pedrelli

In this paper, we introduce a novel approach for diagnosis of Parkinson's Disease (PD) based on deep Echo State Networks (ESNs).

Time Series

Deep Echo State Network (DeepESN): A Brief Survey

4 code implementations12 Dec 2017 Claudio Gallicchio, Alessio Micheli

The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community.

Hierarchical Temporal Representation in Linear Reservoir Computing

no code implementations16 May 2017 Claudio Gallicchio, Alessio Micheli, Luca Pedrelli

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs).

Cannot find the paper you are looking for? You can Submit a new open access paper.