Zobrazeno 1 - 10
of 361
pro vyhledávání: '"James Demmel"'
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 44:559-591
We introduce a Generalized LU-Factorization (\textbf{GLU}) for low-rank matrix approximation. We relate this to past approaches and extensively analyze its approximation properties. The established deterministic guarantees are combined with sketching
Autor:
James Demmel
Publikováno v:
SIAM Journal on Matrix Analysis and Applications. 44:408-413
Publikováno v:
CCF Transactions on High Performance Computing. 3:252-270
Kernel ridge regression (KRR) is a fundamental method in machine learning. Given an n-by-d data matrix as input, a traditional implementation requires $$\Theta (n^2)$$ memory to form an n-by-n kernel matrix and $$\Theta (n^3)$$ flops to compute the f
Publikováno v:
Proceedings of the Platform for Advanced Scientific Computing Conference.
Convolutional neural networks (CNNs) are important in a wide variety of machine learning tasks and applications, so optimizing their performance is essential. Moving words of data between levels of a memory hierarchy or between processors on a networ
Publikováno v:
2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS).
Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times Dense Matrix Multiplication (SpMM) appear in diverse settings, such as collaborative filtering, document clustering, and graph embedding. Frequently, the SDDMM output becomes th
Autor:
Surajit Das, Laura Grigori, Kimon Fountoulakis, James Demmel, Michael W. Mahoney, Shenghao Yang
Publikováno v:
SIAM Journal on Scientific Computing. 43:C154-C176
We are interested in parallelizing the Least Angle Regression (LARS) algorithm for fitting linear regression models to high-dimensional data. We consider two parallel and communication avoiding versions of the basic LARS algorithm. The two algorithms
Autor:
James Demmel, Jack Dongarra, Mark Gates, Greg Henry, Julien Langou, Xiaoye Li, Piotr Luszczek, Weslley Pereira, Jason Riedy, Cindy Rubio-Gonzalez
Numerical exceptions, which may be caused by overflow, operations like division by 0 or sqrt(-1), or convergence failures, are unavoidable in many cases, in particular when software is used on unforeseen and difficult inputs. As more aspects of socie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::92d989e36f316b438bd1ed6d0f956dd7
Publikováno v:
ACM Transactions on Mathematical Software. 46:1-49
We define “reproducibility” as getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should not affect the answer. Many users depend on reproducibility for debugg
Autor:
Yuxiong He, Wenhan Wang, Yang You, Kurt Keutzer, James Demmel, Samyam Rajbhandari, Cho-Jui Hsieh
Publikováno v:
Knowledge and Information Systems. 62:4169-4197
Long short-term memory (LSTM) is a powerful deep learning technique that has been widely used in many real-world data-mining applications such as language modeling and machine translation. In this paper, we aim to minimize the latency of LSTM inferen
Publikováno v:
ACM Transactions on Mathematical Software. 46:1-25
The Singular Value Decomposition (SVD) is widely used in numerical analysis and scientific computing applications, including dimensionality reduction, data compression and clustering, and computation of pseudo-inverses. In many cases, a crucial part