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Found 9 papers, 0 papers with code
Deep Layer-wise Networks Have Closed-Form Weights
There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP).
Instance-wise Feature Grouping
Moreover, the features that belong to each group, and the important feature groups may vary per sample.
Kernel Dependence Network
We propose a greedy strategy to spectrally train a deep network for multi-class classification.
Deep Layer-wise Networks Have Closed-Form Weights
There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP).
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.
Iterative Spectral Method for Alternative Clustering
Given a dataset and an existing clustering as input, alternative clustering aims to find an alternative partition.
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.
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.
Deep Kernel Learning for Clustering
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data.
