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08/17/2023
Modeling Edge Features with Deep Bayesian Graph Networks
We propose an extension of the Contextual Graph Markov Model, a deep and...
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05/31/2023
Is Rewiring Actually Helpful in Graph Neural Networks?
Graph neural networks compute node representations by performing multipl...
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05/14/2023
Addressing Heterophily in Node Classification with Graph Echo State Networks
Node classification tasks on graphs are addressed via fully-trained deep...
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12/13/2022
Leave Graphs Alone: Addressing Over-Squashing without Rewiring
Recent works have investigated the role of graph bottlenecks in preventi...
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10/27/2022
Beyond Homophily with Graph Echo State Networks
Graph Echo State Networks (GESN) have already demonstrated their efficac...
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10/16/2021
Dynamic Graph Echo State Networks
Dynamic temporal graphs represent evolving relations between entities, e...
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07/14/2021
TEACHING – Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on th...
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04/20/2021
Phase Transition Adaptation
Artificial Recurrent Neural Networks are a powerful information processi...
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04/10/2021
Pyramidal Reservoir Graph Neural Network
We propose a deep Graph Neural Network (GNN) model that alternates two t...
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12/05/2020
Graph Mixture Density Networks
We introduce the Graph Mixture Density Network, a new family of machine ...
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07/14/2020
Accelerating the identification of informative reduced representations of proteins with deep learning for graphs
The limits of molecular dynamics (MD) simulations of macromolecules are ...
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05/11/2020
Ring Reservoir Neural Networks for Graphs
Machine Learning for graphs is nowadays a research topic of consolidated...
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02/28/2020
A Deep Generative Model for Fragment-Based Molecule Generation
Molecule generation is a challenging open problem in cheminformatics. Cu...
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02/10/2020
Machine learning approaches for identifying prey handling activity in otariid pinnipeds
Systems developed in wearable devices with sensors onboard are widely us...
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01/31/2020
Edge-based sequential graph generation with recurrent neural networks
Graph generation with Machine Learning is an open problem with applicati...
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01/24/2020
Theoretically Expressive and Edge-aware Graph Learning
We propose a new Graph Neural Network that combines recent advancements ...
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12/29/2019
A Gentle Introduction to Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic ...
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12/20/2019
A Fair Comparison of Graph Neural Networks for Graph Classification
Experimental reproducibility and replicability is a critical topic in ma...
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11/20/2019
Fast and Deep Graph Neural Networks
We address the efficiency issue for the construction of a deep graph neu...
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09/24/2019
Reservoir Topology in Deep Echo State Networks
Deep Echo State Networks (DeepESNs) recently extended the applicability ...
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05/15/2019
Embeddings and Representation Learning for Structured Data
Performing machine learning on structured data is complicated by the fac...
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03/12/2019
Richness of Deep Echo State Network Dynamics
Reservoir Computing (RC) is a popular methodology for the efficient desi...
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12/30/2018
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction
We propose an experimental comparison between Deep Echo State Networks (...
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06/13/2018
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Metric learning has the aim to improve classification accuracy by learni...
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05/27/2018
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
We introduce the Contextual Graph Markov Model, an approach combining id...
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02/19/2018
Deep Echo State Networks for Diagnosis of Parkinson's Disease
In this paper, we introduce a novel approach for diagnosis of Parkinson'...
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12/12/2017
Deep Echo State Network (DeepESN): A Brief Survey
The study of deep recurrent neural networks (RNNs) and, in particular, o...
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05/16/2017