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Found 10 papers, 6 papers with code
Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation
To the best of our knowledge, this is the first work that provides a primal-dual representation for the asymmetric kernel in self-attention and successfully applies it to modeling and optimization.
Ranked #2 on
Offline RL
on D4RL
Unsupervised Energy-based Out-of-distribution Detection using Stiefel-Restricted Kernel Machine
Detecting out-of-distribution (OOD) samples is an essential requirement for the deployment of machine learning systems in the real world.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Tensor-based Multi-view Spectral Clustering via Shared Latent Space
In our method, the dual variables, playing the role of hidden features, are shared by all views to construct a common latent space, coupling the views by learning projections from view-specific spaces.
Deep Kernel Principal Component Analysis for Multi-level Feature Learning
Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction.
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms
The goal of this paper is to revisit Kernel Principal Component Analysis (KPCA) through dualization of a difference of convex functions.
Unsupervised learning of disentangled representations in deep restricted kernel machines with orthogonality constraints
We introduce Constr-DRKM, a deep kernel method for the unsupervised learning of disentangled data representations.
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node Classification
We present a deep Graph Convolutional Kernel Machine (GCKM) for semi-supervised node classification in graphs.
Nonlinear SVD with Asymmetric Kernels: feature learning and asymmetric Nyström method
We describe a nonlinear extension of the matrix Singular Value Decomposition through asymmetric kernels, namely KSVD.
Combining Primal and Dual Representations in Deep Restricted Kernel Machines Classifiers
In the context of deep learning with kernel machines, the deep Restricted Kernel Machine (DRKM) framework allows multiple levels of kernel PCA (KPCA) and Least-Squares Support Vector Machines (LSSVM) to be combined into a deep architecture using visible and hidden units.
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes
In this work, we propose Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) for building uncertainty-aware self-attention where the asymmetry of attention kernels is tackled by Kernel SVD (KSVD) and a reduced complexity is acquired.
