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Found 25 papers, 7 papers with code
Edge of stability echo state networks
With the goal of bringing together the fading memory property and the ability to retain as much memory as possible, in this paper we introduce a new ESN architecture, called the Edge of Stability Echo State Network (ES$^2$N).
Anti-Symmetric DGN: a stable architecture for Deep Graph Networks
Deep Graph Networks (DGNs) currently dominate the research landscape of learning from graphs, due to their efficiency and ability to implement an adaptive message-passing scheme between the nodes.
Continual Learning for Human State Monitoring
Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications.
Federated Adaptation of Reservoirs via Intrinsic Plasticity
We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario.
Deep Features for CBIR with Scarce Data using Hebbian Learning
Features extracted from Deep Neural Networks (DNNs) have proven to be very effective in the context of Content Based Image Retrieval (CBIR).
Euler State Networks: Non-dissipative Reservoir Computing
Inspired by the numerical solution of ordinary differential equations, in this paper we propose a novel Reservoir Computing (RC) model, called the Euler State Network (EuSN).
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-Service toolkit developed within the H2020 TEACHING project and designed to implement an autonomous driving personalization system according to the output of an automatic driver's stress recognition algorithm, both of them realizing a Cyber-Physical System of Systems.
TEACHING -- Trustworthy autonomous cyber-physical applications through human-centred intelligence
This paper discusses the perspective of the H2020 TEACHING project on the next generation of autonomous applications running in a distributed and highly heterogeneous environment comprising both virtual and physical resources spanning the edge-cloud continuum.
Continual Learning with Echo State Networks
Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge.
Phase Transition Adaptation
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
We propose a deep Graph Neural Network (GNN) model that alternates two types of layers.
Sparsity in Reservoir Computing Neural Networks
Our results point out that sparsity, in particular in input-reservoir connections, has a major role in developing internal temporal representations that have a longer short-term memory of past inputs and a higher dimension.
Ring Reservoir Neural Networks for Graphs
Machine Learning for graphs is nowadays a research topic of consolidated relevance.
Deep Randomized Neural Networks
For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains.
Fast and Deep Graph Neural Networks
We address the efficiency issue for the construction of a deep graph neural network (GNN).
Reservoir Topology in Deep Echo State Networks
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
Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form.
Richness of Deep Echo State Network Dynamics
Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs).
Comparison between DeepESNs and gated RNNs on multivariate time-series prediction
The analysis is performed in terms of efficiency and prediction accuracy on 4 polyphonic music tasks.
Chasing the Echo State Property
Reservoir Computing (RC) provides an efficient way for designing dynamical recurrent neural models.
Tree Edit Distance Learning via Adaptive Symbol Embeddings
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.
Deep Echo State Networks for Diagnosis of Parkinson's Disease
In this paper, we introduce a novel approach for diagnosis of Parkinson's Disease (PD) based on deep Echo State Networks (ESNs).
Short-term Memory of Deep RNN
The extension of deep learning towards temporal data processing is gaining an increasing research interest.
Deep Echo State Network (DeepESN): A Brief Survey
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
Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs).
