
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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Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
Graph representation learning has become a ubiquitous component in many ...
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A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
Nonlinear models are known to provide excellent performance in realworl...
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Distributed Graph Convolutional Networks
The aim of this work is to develop a fullydistributed algorithmic frame...
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PseudoRehearsal for Continual Learning with Normalizing Flows
Catastrophic forgetting (CF) happens whenever a neural network overwrite...
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Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation
We study distributed stochastic nonconvex optimization in multiagent ne...
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Why should we add early exits to neural networks?
Deep neural networks are generally designed as a stack of differentiable...
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Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Bayesian Neural Networks (BNNs) are trained to optimize an entire distri...
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Deep Randomized Neural Networks
Randomized Neural Networks explore the behavior of neural systems where ...
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Adaptive Propagation Graph Convolutional Network
Graph convolutional networks (GCNs) are a family of neural network model...
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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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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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Compressing deep quaternion neural networks with targeted regularization
In recent years, hypercomplex deep networks (e.g., quaternionbased) ha...
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Efficient data augmentation using graph imputation neural networks
Recently, data augmentation in the semisupervised regime, where unlabel...
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Missing Data Imputation with Adversariallytrained Graph Convolutional Networks
Missing data imputation (MDI) is a fundamental problem in many scientifi...
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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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Widely Linear Kernels for ComplexValued Kernel Activation Functions
Complexvalued neural networks (CVNNs) have been shown to be powerful no...
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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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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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Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
Gated recurrent neural networks have achieved remarkable results in the ...
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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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Improving Graph Convolutional Networks with NonParametric Activation Functions
Graph neural networks (GNNs) are a class of neural networks that allow t...
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Complexvalued Neural Networks with Nonparametric Activation Functions
Complexvalued neural networks (CVNNs) are a powerful modeling tool for ...
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Bidirectional deepreadout echo state networks
We propose a deep architecture for the classification of multivariate ti...
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Bidirectional deepreservoir echo state networks
We propose a deep architecture for the classification of multivariate ti...
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Bidirectional deep echo state networks
In this work we propose a deep architecture for the classification of mu...
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Kafnets: kernelbased nonparametric activation functions for neural networks
Neural networks are generally built by interleaving (adaptable) linear l...
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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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Recursive Multikernel Filters Exploiting Nonlinear Temporal Structure
In kernel methods, temporal information on the data is commonly included...
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Adaptation and learning over networks for nonlinear system modeling
In this chapter, we analyze nonlinear filtering problems in distributed ...
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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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Distributed Supervised Learning using Neural Networks
Distributed learning is the problem of inferring a function in the case ...
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Group Sparse Regularization for Deep Neural Networks
In this paper, we consider the joint task of simultaneously optimizing (...
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Learning activation functions from data using cubic spline interpolation
Neural networks require a careful design in order to perform properly on...
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Simone Scardapane
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Postdoctoral Fellow at Università degli Studi di Roma 'La Sapienza' since 2016, CoFounder at Italian Association for Machine Learning since 2017, CoOrganizer at Machine Learning & Data Science Meetup Roma since 2016, Cognitive Big Data Informatics (CogBID) Honorary Research Fellow at University of Stirling since 2015, Lecturer at Università degli Studi di Perugia 2017, Occasional Collaborator at University of Rome "La Sapienza" 20132014, Software Developer at 5 Emme Informatica S.p.A. 20112012