Simone Scardapane

Class Incremental Learning with probability dampening and cascaded gated classifier
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Adaptive Semantic Token Selection for AI-native Goal-oriented Communications
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Adaptive Point Transformer
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Goal-Oriented and Semantic Communication in 6G AI-Native Networks: The 6G-GOALS Approach
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NACHOS: Neural Architecture Search for Hardware-Constrained Early-Exit Neural Networks
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Position: Topological Deep Learning is the New Frontier for Relational Learning
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Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production
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Conditional computation in neural networks: Principles and research trends
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Alice's Adventures in a Differentiable Wonderland - Volume I, A Tour of the Land
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From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module
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Reidentification of Objects From Aerial Photos With Hybrid Siamese Neural Networks
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Rearranging Pixels is a Powerful Black-Box Attack for RGB and Infrared Deep Learning Models
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Continual Self-Supervised Learning in Earth Observation with Embedding Regularization
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Adaptive Computation Modules: Granular Conditional Computation For Efficient Inference
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Tracin in Semantic Segmentation of Tumor Brains in MRI, an Extended Approach (SHORT PAPER)
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On the robustness of vision transformers for in-flight monocular depth estimation
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Machine Un-learning: An Overview of Techniques, Applications, and Future Directions
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GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
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Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability
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Hypergraph Neural Networks through the Lens of Message Passing: A Common Perspective to Homophily and Architecture Design
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Convolutional Neural Networks for the Detection of Esca Disease Complex in Asymptomatic Grapevine Leaves
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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence
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Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks
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Convergent Approaches to AI Explainability for HEP Muonic Particles Pattern Recognition
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Probabilistic Load Forecasting With Reservoir Computing
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ArcheoWeedNet: Weed Classification in the Parco archeologico del Colosseo
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ICML 2023 Topological Deep Learning Challenge : Design and Results
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Self-supervised learning for medieval handwriting identification: A case study from the Vatican Apostolic Library
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Pixle: a fast and effective black-box attack based on rearranging pixels
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Continual Barlow Twins: Continual Self-Supervised Learning for Remote Sensing Semantic Segmentation
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Towards Self-Supervised Gaze Estimation
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EGG-GAE: scalable graph neural networks for tabular data imputation
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Engagement Detection with Multi-Task Training in E-Learning Environments
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Inferring 3D change detection from bitemporal optical images
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Explainability in subgraphs-enhanced Graph Neural Networks
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Learning Speech Emotion Representations in the Quaternion Domain
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Handwriting Declines With Human Aging: A Machine Learning Study
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Multi-site Forecasting of Energy Time Series with Spatio-Temporal Graph Neural Networks
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Continual Learning with Invertible Generative Models
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Centroids Matching: an efficient Continual Learning approach operating in the embedding space
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Evaluating Adversarial Attacks and Defences in Infrared Deep Learning Monitoring Systems
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Guest Editorial: Trends in Reservoir Computing
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FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning
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MARE: Self-Supervised Multi-Attention REsu-Net for Semantic Segmentation in Remote Sensing
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Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
20219 citationsauthor #2
A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models
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A Calibrated Multiexit Neural Network for Detecting Urothelial Cancer Cells
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Avalanche: an End-to-End Library for Continual Learning
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A New Class of Efficient Adaptive Filters for Online Nonlinear Modeling
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A Meta-Learning Approach for Training Explainable Graph Neural Networks
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A Wide Multimodal Dense U-Net for Fast Magnetic Resonance Imaging
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Differentiable Branching In Deep Networks for Fast Inference
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Deep Randomized Neural Networks
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Music Genre Classification Using Stacked Auto-Encoders
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Quaternion Neural Networks for 3D Sound Source Localization in Reverberant Environments
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Why Should We Add Early Exits to Neural Networks?
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Distributed Graph Convolutional Networks
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Recognition of marine seismic data features using convolutional neural networks
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Pseudo-Rehearsal for Continual Learning with Normalizing Flows
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Paper Index
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Distributed Training of Graph Convolutional Networks
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Adaptive Propagation Graph Convolutional Network
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A non-parametric softmax for improving neural attention in time-series forecasting
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Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
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Frontiers in Reservoir Computing
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Optimized training and scalable implementation of Conditional Deep Neural Networks with early exits for Fog-supported IoT applications
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Distributed Stochastic Nonconvex Optimization and Learning based on Successive Convex Approximation
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Compressing deep quaternion neural networks with targeted regularization
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Missing Data Imputation with Adversarially-trained Graph Convolutional Networks
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On the Stability and Generalization of Learning with Kernel Activation Functions
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A Multimodal Deep Network for the Reconstruction of T2W MR Images
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Widely Linear Kernels for Complex-valued Kernel Activation Functions
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Efficient Continual Learning in Neural Networks with Embedding Regularization
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Efficient data augmentation using graph imputation neural networks
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Kafnets: Kernel-based non-parametric activation functions for neural networks
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A Multimodal Dense U-Net For Accelerating Multiple Sclerosis MRI
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Multikernel activation functions: formulation and a case study
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Improving Graph Convolutional Networks with Non-Parametric Activation Functions
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Sparse functional link adaptive filter using an ℓ1-norm regularization
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Complex-Valued Neural Networks With Nonparametric Activation Functions
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RECURRENT NEURAL NETWORKS WITH FLEXIBLE GATES USING KERNEL ACTIVATION FUNCTIONS
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Bayesian Random Vector Functional-Link Networks for Robust Data Modeling
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Quaternion Convolutional Neural Networks for Detection and Localization of 3D Sound Events
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Reservoir Computing Approaches for Representation and Classification of Multivariate Time Series
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Combined Sparse Regularization for Nonlinear Adaptive Filters
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Privacy-Preserving Data Mining for Distributed Medical Scenarios
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On the use of deep recurrent neural networks for detecting audio spoofing attacks
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Adaptation and learning over networks for nonlinear system modeling
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Fully Decentralized Semi-supervised Learning via Privacy-preserving Matrix Completion
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Semi-supervised Echo State Networks for Audio Classification
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A Low-Complexity Linear-in-the-Parameters Nonlinear Filter for Distorted Speech Signals
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Recursive multikernel filters exploiting nonlinear temporal structure
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Randomness in neural networks: an overview
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Separation of Drum and Bass from Monaural Tracks
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In Codice Ratio: OCR of Handwritten Latin Documents using Deep Convolutional Networks
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Bidirectional deep echo state networks
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Flexible Generative Adversarial Networks with Non-parametric Activation Functions
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Bidirectional deep-readout echo state networks
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Stochastic Training of Neural Networks via Successive Convex Approximations
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Advances in Biologically Inspired Reservoir Computing
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