Search Results for author: Thomas D. Ahle

Found 5 papers, 1 papers with code

Favour: FAst Variance Operator for Uncertainty Rating

no code implementations21 Nov 2023 Thomas D. Ahle, Sahar Karimi, Peter Tak Peter Tang

Our contribution is a more principled variance propagation framework based on "spiked covariance matrices", which smoothly interpolates between quality and inference time.

Variational Inference

Similarity Search with Tensor Core Units

no code implementations22 Jun 2020 Thomas D. Ahle, Francesco Silvestri

Tensor Core Units (TCUs) are hardware accelerators developed for deep neural networks, which efficiently support the multiplication of two dense $\sqrt{m}\times \sqrt{m}$ matrices, where $m$ is a given hardware parameter.

Dimensionality Reduction

Almost Optimal Tensor Sketch

no code implementations3 Sep 2019 Thomas D. Ahle, Jakob B. T. Knudsen

With another construction we get $\lambda$ times more rows $m=\tilde O(c\,\lambda^2\,\varepsilon^{-2}(\log1/\delta)^3)$, but the matrix can be applied to any vector $x^{(1)}\otimes\dots\otimes x^{(c)}\in R^{d^c}$ in just $\tilde O(c\, (d+m))$ time.

Oblivious Sketching of High-Degree Polynomial Kernels

1 code implementation3 Sep 2019 Thomas D. Ahle, Michael Kapralov, Jakob B. T. Knudsen, Rasmus Pagh, Ameya Velingker, David Woodruff, Amir Zandieh

Oblivious sketching has emerged as a powerful approach to speeding up numerical linear algebra over the past decade, but our understanding of oblivious sketching solutions for kernel matrices has remained quite limited, suffering from the aforementioned exponential dependence on input parameters.

Data Structures and Algorithms

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