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02/05/2022
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
Deep learning (DL) has been widely investigated in a vast majority of ap...
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01/15/2022
Deep Optimal Transport on SPD Manifolds for Domain Adaptation
The domain adaption (DA) problem on symmetric positive definite (SPD) ma...
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03/16/2021
Ternary Hashing
This paper proposes a novel ternary hash encoding for learning to hash m...
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11/27/2020
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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08/16/2020
Geometric Foundations of Data Reduction
The purpose of this paper is to write a complete survey of the (spectral...
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07/03/2020
Privacy Threats Against Federated Matrix Factorization
Matrix Factorization has been very successful in practical recommendatio...
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06/20/2020
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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06/15/2020
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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04/26/2020
Federated Transfer Learning for EEG Signal Classification
The success of deep learning (DL) methods in the Brain-Computer Interfac...
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09/11/2019
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
Electroencephalography (EEG) classification techniques have been widely ...
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05/21/2019
Stochastic Inverse Reinforcement Learning
Inverse reinforcement learning (IRL) is an ill-posed inverse problem sin...
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02/28/2019
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding dynamic obstacles (vehicles, bicyc...
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12/17/2018
Representation Learning for Spatial Graphs
Recently, the topic of graph representation learning has received plenty...
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09/14/2018