Zobrazeno 1 - 10
of 49
pro vyhledávání: '"SKAU, ERIK"'
Autor:
Vu, Minh, Nebgen, Ben, Skau, Erik, Zollicoffer, Geigh, Castorena, Juan, Rasmussen, Kim, Alexandrov, Boian, Bhattarai, Manish
As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its resilience to such attacks is Non-negative Matrix Factor
Externí odkaz:
http://arxiv.org/abs/2408.03909
Monitoring of industrial processes is a critical capability in industry and in government to ensure reliability of production cycles, quick emergency response, and national security. Process monitoring allows users to gauge the progress of an organiz
Externí odkaz:
http://arxiv.org/abs/2210.01060
Autor:
Boureima, Ismael, Bhattarai, Manish, Eren, Maksim, Skau, Erik, Romero, Philip, Eidenbenz, Stephan, Alexandrov, Boian
We propose an efficient distributed out-of-memory implementation of the Non-negative Matrix Factorization (NMF) algorithm for heterogeneous high-performance-computing (HPC) systems. The proposed implementation is based on prior work on NMFk, which ca
Externí odkaz:
http://arxiv.org/abs/2202.09518
Autor:
Bhattarai, Manish, Kharat, Namita, Skau, Erik, Nebgen, Benjamin, Djidjev, Hristo, Rajopadhye, Sanjay, Smith, James P., Alexandrov, Boian
With the boom in the development of computer hardware and software, social media, IoT platforms, and communications, there has been an exponential growth in the volume of data produced around the world. Among these data, relational datasets are growi
Externí odkaz:
http://arxiv.org/abs/2202.09512
We utilize a recently developed topic modeling method called SeNMFk, extending the standard Non-negative Matrix Factorization (NMF) methods by incorporating the semantic structure of the text, and adding a robust system for determining the number of
Externí odkaz:
http://arxiv.org/abs/2201.00687
A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with the constraint over an auxiliary matrix whose Boolean structure is identica
Externí odkaz:
http://arxiv.org/abs/2106.04708
The application of binary matrices are numerous. Representing a matrix as a mixture of a small collection of latent vectors via low-rank decomposition is often seen as an advantageous method to interpret and analyze data. In this work, we examine the
Externí odkaz:
http://arxiv.org/abs/2012.10496
Autor:
Bhattarai, Manish, Chennupati, Gopinath, Skau, Erik, Vangara, Raviteja, Djidjev, Hirsto, Alexandrov, Boian
The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exaflops also come the extra-large high-dimensional data generated by high-performance computing.
Externí odkaz:
http://arxiv.org/abs/2008.01340
Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, it
Externí odkaz:
http://arxiv.org/abs/2003.00129
There is an emerging interest in tensor factorization applications in big-data analytics and machine learning. To speed up the factorization of extra-large datasets, organized in multidimensional arrays (aka tensors), easy to compute compression-base
Externí odkaz:
http://arxiv.org/abs/1909.07570