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Found 16 papers, 1 papers with code
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection
We consider two cases (a) when a single student is learnt for all the patients using preselected channels; and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbelsoftmax approach.
A Meta-GNN approach to personalized seizure detection and classification
In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples.
Learning Models of Model Predictive Controllers using Gradient Data
As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension.
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
Our hypothesis is that the use of tasksimilarity helps meta-learning when the available tasks are limited and may contain outlier/ dissimilar tasks.
On Training and Evaluation of Neural Network Approaches for Model Predictive Control
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks.
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University.
High-dimensional Neural Feature Design for Layer-wise Reduction of Training Cost
We show that the proposed architecture is norm-preserving and provides an invertible feature vector, and therefore, can be used to reduce the training cost of any other learning method which employs linear projection to estimate the target.
Recursive Prediction of Graph Signals with Incoming Nodes
Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph.
Learning sparse linear dynamic networks in a hyper-parameter free setting
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting.
Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise
In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph.
Gaussian Processes Over Graphs
We propose Gaussian processes for signals over graphs (GPG) using the apriori knowledge that the target vectors lie over a graph.
Multi-kernel Regression For Graph Signal Processing
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph.
Extreme Learning Machine for Graph Signal Processing
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization.
R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network
We consider a neural network architecture with randomized features, a sign-splitter, followed by rectified linear units (ReLU).
Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
We address the problem of prediction of multivariate data process using an underlying graph model.
A Connectedness Constraint for Learning Sparse Graphs
We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning.
