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08/05/2023
Edge of stability echo state networks
In this paper, we propose a new Reservoir Computing (RC) architecture, c...
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10/18/2022
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
Deep Graph Networks (DGNs) currently dominate the research landscape of ...
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06/29/2022
Continual Learning for Human State Monitoring
Continual Learning (CL) on time series data represents a promising but u...
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05/25/2022
Federated Adaptation of Reservoirs via Intrinsic Plasticity
We propose a novel algorithm for performing federated learning with Echo...
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05/18/2022
Deep Features for CBIR with Scarce Data using Hebbian Learning
Features extracted from Deep Neural Networks (DNNs) have proven to be ve...
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03/17/2022
Euler State Networks
Inspired by the numerical solution of ordinary differential equations, i...
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02/03/2022
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving
This paper presents a proof-of-concept implementation of the AI-as-a-Ser...
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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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05/17/2021
Continual Learning with Echo State Networks
Continual Learning (CL) refers to a learning setup where data is non sta...
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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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06/04/2020
Sparsity in Reservoir Computing Neural Networks
Reservoir Computing (RC) is a well-known strategy for designing Recurren...
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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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02/27/2020
Deep Randomized Neural Networks
Randomized Neural Networks explore the behavior of neural systems where ...
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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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11/27/2018
Chasing the Echo State Property
Reservoir Computing (RC) provides an efficient way for designing dynamic...
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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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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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02/02/2018
Short-term Memory of Deep RNN
The extension of deep learning towards temporal data processing is gaini...
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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
