New asymmetric kernel methods improve feature learning.
problem Improving feature learning with asymmetric kernels.
method Coupled covariance eigenproblem and Nyström method.
result Empirical evaluations show benefits of KSVD.
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3 results for “kernel-svd”
A new method for self-attention models that improves uncertainty estimation.
problem Overconfident predictions and lack of calibrated uncertainty in Transformers.
method Kernel-Eigen Pair Sparse Variational Gaussian Processes (KEP-SVGP) with Kernel SVD (KSVD) to handle asymmetry of attention kernels.
result Reduction in time complexity and improved performance on various benchmarks.
New algorithm reduces kernel optimization complexity.
problem Efficiently learn semiseparable kernels for robust machine learning.
method Proposed a SVD-QCQP primal-dual algorithm for minimax optimization.
result Significant improvement in accuracy over non-convex approaches.