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Found 7 papers, 0 papers with code
Convolution-Free Waveform Transformers for Multi-Lead ECG Classification
We present our entry to the 2021 PhysioNet/CinC challenge - a waveform transformer model to detect cardiac abnormalities from ECG recordings.
Interpretable Additive Recurrent Neural Networks For Multivariate Clinical Time Series
Importantly, the hidden state activations represent feature coefficients that correlate with the prediction target and can be visualized as risk curves that capture the global relationship between individual input features and the outcome.
Solving Interpretable Kernel Dimensionality Reduction
While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret.
Solving Interpretable Kernel Dimension Reduction
While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret.
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning.
Deep Kernel Learning for Clustering
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data.
Multiple Clustering Views from Multiple Uncertain Experts
In this paper, we address the problem on how to automatically discover multiple ways to cluster data given potentially diverse inputs from multiple uncertain experts.
