no code implementations • 21 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.
no code implementations • 22 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.
no code implementations • 3 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.
1 code implementation • 3 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
no code implementations • 9 Oct 2015 • Thomas D. Ahle, Rasmus Pagh, Ilya Razenshteyn, Francesco Silvestri
* New upper and lower bounds for (A)LSH-based algorithms.