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pro vyhledávání: '"Ahle, Thomas D."'
Bayesian Neural Networks (BNN) have emerged as a crucial approach for interpreting ML predictions. By sampling from the posterior distribution, data scientists may estimate the uncertainty of an inference. Unfortunately many inference samples are oft
Externí odkaz:
http://arxiv.org/abs/2311.13036
Autor:
Ahle, Thomas D.
We prove the inequality $E[(X/\mu)^k] \le (\frac{k/\mu}{\log(k/\mu+1)})^k \le \exp(k^2/(2\mu))$ for sub-Poissonian random variables, such as Binomially or Poisson distributed random variables with mean $\mu$. The asymptotics $1+O(k^2/\mu)$ can be sho
Externí odkaz:
http://arxiv.org/abs/2103.17027
A polyomino is a polygonal region with axis parallel edges and corners of integral coordinates, which may have holes. In this paper, we consider planar tiling and packing problems with polyomino pieces and a polyomino container $P$. We give two polyn
Externí odkaz:
http://arxiv.org/abs/2011.10983
Autor:
Ahle, Thomas D., Silvestri, Francesco
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. In this paper, we show that
Externí odkaz:
http://arxiv.org/abs/2006.12608
Autor:
Ahle, Thomas D., Knudsen, Jakob B. T.
We construct a matrix $M\in R^{m\otimes d^c}$ with just $m=O(c\,\lambda\,\varepsilon^{-2}\text{poly}\log1/\varepsilon\delta)$ rows, which preserves the norm $\|Mx\|_2=(1\pm\varepsilon)\|x\|_2$ of all $x$ in any given $\lambda$ dimensional subspace of
Externí odkaz:
http://arxiv.org/abs/1909.01821
Autor:
Ahle, Thomas D., Kapralov, Michael, Knudsen, Jakob B. T., Pagh, Rasmus, Velingker, Ameya, Woodruff, David, Zandieh, Amir
Kernel methods are fundamental tools in machine learning that allow detection of non-linear dependencies between data without explicitly constructing feature vectors in high dimensional spaces. A major disadvantage of kernel methods is their poor sca
Externí odkaz:
http://arxiv.org/abs/1909.01410
We present a data structure for *spherical range reporting* on a point set $S$, i.e., reporting all points in $S$ that lie within radius $r$ of a given query point $q$. Our solution builds upon the Locality-Sensitive Hashing (LSH) framework of Indyk
Externí odkaz:
http://arxiv.org/abs/1605.02673
A number of tasks in classification, information retrieval, recommendation systems, and record linkage reduce to the core problem of inner product similarity join (IPS join): identifying pairs of vectors in a collection that have a sufficiently large
Externí odkaz:
http://arxiv.org/abs/1510.02824
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