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Rethinking Uncertainty in Deep Learning: Whether and How it Improves Robustness
Deep neural networks (DNNs) are known to be prone to adversarial attacks...
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Geometric Foundations of Data Reduction
The purpose of this paper is to write a complete survey of the (spectral...
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Privacy Threats Against Federated Matrix Factorization
Matrix Factorization has been very successful in practical recommendatio...
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Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks
This paper investigates capabilities of Privacy-Preserving Deep Learning...
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Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention
prevention of stroke with its associated risk factors has been one of th...
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Federated Transfer Learning for EEG Signal Classification
The success of deep learning (DL) methods in the Brain-Computer Interfac...
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HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
Electroencephalography (EEG) classification techniques have been widely ...
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Stochastic Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is an ill-posed inverse problem sin...
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Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding dynamic obstacles (vehicles, bicyc...
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Representation Learning for Spatial Graphs
Recently, the topic of graph representation learning has received plenty...
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Socially Aware Kalman Neural Networks for Trajectory Prediction
Trajectory prediction is a critical technique in the navigation of robot...
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