SmartTensors AI delivers a comprehensive suite of software solutions tailored for in-depth analysis of vast datasets, accurate and precise extraction of hidden patterns, harnessing the power of high-performance computing and cutting-edge GPU architectures. Our approach is underpinned by scalable and highly efficient algorithms. We provide array of libraries targeted to diverse set of problems including data compression, computer security, and pattern analysis.
HPC Solutions
pyDNMFk
pyDNMFk is a software package for applying non-negative matrix factorization in a distributed fashion to large datasets. It has the ability to minimize the difference between reconstructed data and the original data through various norms (Frobenious, KL-divergence).
pyDNTNK
pyDNTNK is a software package for applying non-negative Hierarchical Tensor decompositions such as Tensor train and Hierarchical Tucker decompositons in a distributed fashion to large datasets. It is built on top of pyDNMFk.
cuda-pyDNMFk
Cuda Python Distributed Non Negative Matrix Factorization with determination of hidden features. cuda-pyDNMFk is a dynamic software platform tailored for the decomposition of large datasets that surpass the limitations of in-memory processing.
pyDRESCALk
pyDRESCALk is a software package for applying non-negative RESCAL decomposition in a distributed fashion to large datasets. It can be utilized for decomposing relational datasets.
Feature Extraction
T-ELF
Tensor Extraction of Latent Features (T-ELF) is one of the machine learning software packages developed as part of the R&D 100 winning SmartTensors AI project at Los Alamos National Laboratory (LANL). T-ELF presents an array of customizable software solutions crafted for analysis of datasets. Acting as a comprehensive toolbox, T-ELF specializes in data pre-processing, extraction of latent features, and structuring results to facilitate informed decision-making. Leveraging high-performance computing and cutting-edge GPU architectures, our toolbox is optimized for analyzing large datasets from diverse set of problems.
Central to T-ELF's core capabilities lie non-negative matrix and tensor factorization solutions for discovering multi-faceted hidden details in data, featuring automated model determination facilitating the estimation of latent factors or rank. This pivotal functionality ensures precise data modeling and the extraction of concealed patterns. Additionally, our software suite incorporates cutting-edge modules for both pre-processing and post-processing of data, tailored for diverse tasks including text mining, Natural Language Processing, and robust tools for matrix and tensor analysis and construction.
T-ELF's adaptability spans across a multitude of disciplines, positioning it as a robust AI and data analytics solution. Its proven efficacy extends across various fields such as Large-scale Text Mining, High Performance Computing, Computer Security, Applied Mathematics, Dynamic Networks and Ranking, Biology, Material Science, Medicine, Chemistry, Data Compression, Climate Studies, Relational Databases, Data Privacy, Economy, and Agriculture.
pyHNMFk
The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state-variable data records. pyHNMFk synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of advection-diffusion equation to identify their characteristics.
