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pro vyhledávání: '"Dunlavy, Daniel M."'
We introduce a new tensor norm, the average spectrum norm, to study sample complexity of tensor completion problems based on the canonical polyadic decomposition (CPD). Properties of the average spectrum norm and its dual norm are investigated, demon
Externí odkaz:
http://arxiv.org/abs/2404.10085
We extend an existing approach for efficient use of shared mapped memory across Chapel and C++ for graph data stored as 1-D arrays to sparse tensor data stored using a combination of 2-D and 1-D arrays. We describe the specific extensions that provid
Externí odkaz:
http://arxiv.org/abs/2310.10872
We employ pressure point analysis and roofline modeling to identify performance bottlenecks and determine an upper bound on the performance of the Canonical Polyadic Alternating Poisson Regression Multiplicative Update (CP-APR MU) algorithm in the Sp
Externí odkaz:
http://arxiv.org/abs/2307.03276
Autor:
Myers, Jeremy M., Dunlavy, Daniel M.
There is growing interest to extend low-rank matrix decompositions to multi-way arrays, or tensors. One fundamental low-rank tensor decomposition is the canonical polyadic decomposition (CPD). The challenge of fitting a low-rank, nonnegative CPD mode
Externí odkaz:
http://arxiv.org/abs/2207.14341
We propose a novel statistical inference methodology for multiway count data that is corrupted by false zeros that are indistinguishable from true zero counts. Our approach consists of zero-truncating the Poisson distribution to neglect all zero valu
Externí odkaz:
http://arxiv.org/abs/2201.10014
Tensor decomposition models play an increasingly important role in modern data science applications. One problem of particular interest is fitting a low-rank Canonical Polyadic (CP) tensor decomposition model when the tensor has sparse structure and
Externí odkaz:
http://arxiv.org/abs/2012.01520
Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS)
Externí odkaz:
http://arxiv.org/abs/2009.10644
We explore the use of multiple deep learning models for detecting flaws in software programs. Current, standard approaches for flaw detection rely on a single representation of a software program (e.g., source code or a program binary). We illustrate
Externí odkaz:
http://arxiv.org/abs/2009.04549
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Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in th
Externí odkaz:
http://arxiv.org/abs/1906.11133