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08/24/2023
Probabilistic load forecasting with Reservoir Computing
Some applications of deep learning require not only to provide accurate ...
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05/25/2023
From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networ...
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04/16/2023
GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
Land cover maps are a pivotal element in a wide range of Earth Observati...
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04/14/2023
Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
Subgraph-enhanced graph neural networks (SGNN) can increase the expressi...
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02/22/2023
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
The rise of graph representation learning as the primary solution for ma...
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10/19/2022
EGG-GAE: scalable graph neural networks for tabular data imputation
Missing data imputation (MDI) is crucial when dealing with tabular datas...
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09/16/2022
Explainability in subgraphs-enhanced Graph Neural Networks
Recently, subgraphs-enhanced Graph Neural Networks (SGNNs) have been int...
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08/03/2022
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
Catastrophic forgetting (CF) occurs when a neural network loses the info...
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05/31/2022
Inferring 3D change detection from bitemporal optical images
Change detection is one of the most active research areas in Remote Sens...
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05/23/2022
Continual Barlow Twins: continual self-supervised learning for remote sensing semantic segmentation
In the field of Earth Observation (EO), Continual Learning (CL) algorith...
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04/08/2022
Engagement Detection with Multi-Task Training in E-Learning Environments
Recognition of user interaction, in particular engagement detection, bec...
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04/05/2022
Learning Speech Emotion Representations in the Quaternion Domain
The modeling of human emotion expression in speech signals is an importa...
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03/21/2022
Towards Self-Supervised Gaze Estimation
Recent joint embedding-based self-supervised methods have surpassed stan...
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02/11/2022
Continual Learning with Invertible Generative Models
Catastrophic forgetting (CF) happens whenever a neural network overwrite...
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02/04/2022
Pixle: a fast and effective black-box attack based on rearranging pixels
Recent research has found that neural networks are vulnerable to several...
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09/20/2021
A Meta-Learning Approach for Training Explainable Graph Neural Networks
In this paper, we investigate the degree of explainability of graph neur...
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05/06/2021
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
In this paper, we propose a novel ensembling technique for deep neural n...
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04/29/2021
Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Graph representation learning has become a ubiquitous component in many ...
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04/19/2021
A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
Nonlinear models are known to provide excellent performance in real-worl...
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07/13/2020
Distributed Graph Convolutional Networks
The aim of this work is to develop a fully-distributed algorithmic frame...
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07/05/2020
Pseudo-Rehearsal for Continual Learning with Normalizing Flows
Catastrophic forgetting (CF) happens whenever a neural network overwrite...
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04/30/2020
Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation
We study distributed stochastic nonconvex optimization in multi-agent ne...
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04/27/2020
Why should we add early exits to neural networks?
Deep neural networks are generally designed as a stack of differentiable...
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03/02/2020
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Bayesian Neural Networks (BNNs) are trained to optimize an entire distri...
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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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02/24/2020
Adaptive Propagation Graph Convolutional Network
Graph convolutional networks (GCNs) are a family of neural network model...
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09/09/2019
Efficient Continual Learning in Neural Networks with Embedding Regularization
Continual learning of deep neural networks is a key requirement for scal...
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08/08/2019
A Multimodal Deep Network for the Reconstruction of T2W MR Images
Multiple sclerosis is one of the most common chronic neurological diseas...
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07/26/2019
Compressing deep quaternion neural networks with targeted regularization
In recent years, hyper-complex deep networks (e.g., quaternion-based) ha...
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06/20/2019
Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semi-supervised regime, where unlabel...
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05/06/2019
Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
Missing data imputation (MDI) is a fundamental problem in many scientifi...
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03/28/2019
On the Stability and Generalization of Learning with Kernel Activation Functions
In this brief we investigate the generalization properties of a recently...
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02/06/2019
Widely Linear Kernels for Complex-Valued Kernel Activation Functions
Complex-valued neural networks (CVNNs) have been shown to be powerful no...
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01/29/2019
Multikernel activation functions: formulation and a case study
The design of activation functions is a growing research area in the fie...
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12/17/2018
Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events
Learning from data in the quaternion domain enables us to exploit intern...
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07/11/2018
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the ...
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03/21/2018
Reservoir computing approaches for representation and classification of multivariate time series
Classification of multivariate time series (MTS) has been tackled with a...
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02/26/2018
Improving Graph Convolutional Networks with Non-Parametric Activation Functions
Graph neural networks (GNNs) are a class of neural networks that allow t...
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02/22/2018
Complex-valued Neural Networks with Non-parametric Activation Functions
Complex-valued neural networks (CVNNs) are a powerful modeling tool for ...
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11/17/2017
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate ti...
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11/17/2017
Bidirectional deep-reservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
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11/17/2017
Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
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07/13/2017
Kafnets: kernel-based non-parametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear l...
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06/15/2017
Stochastic Training of Neural Networks via Successive Convex Approximations
This paper proposes a new family of algorithms for training neural netwo...
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06/12/2017
Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
In kernel methods, temporal information on the data is commonly included...
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04/28/2017
Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed ...
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10/24/2016
A Framework for Parallel and Distributed Training of Neural Networks
The aim of this paper is to develop a general framework for training neu...
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07/21/2016
Distributed Supervised Learning using Neural Networks
Distributed learning is the problem of inferring a function in the case ...
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07/02/2016
Group Sparse Regularization for Deep Neural Networks
In this paper, we consider the joint task of simultaneously optimizing (...
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05/18/2016