SmartTensors AI

The SmartTensors AI Platform is a patented, scalable, unsupervised machine learning software suite capable of identifying, extracting essential hidden features, and efficiently compressing information in massive datasets. SmartTensors autonomously analyzes and discovers hidden features, signatures, and patterns otherwise undetectable and buried in tens of terabytes of data.

Capabilties

Large-scale Text Mining logo

Large-scale Text Mining

Semantic topic modeling, topic evolution, scientific knowladge graph generation with human in the loop procedure, and scientific leadership identification and characterization.

High Performance Computing logo

High Performance Computing

Exascale data analytics, dimension reduction, hidden feature extraction, and efficent and scalable algorithms in emerging computing architectures.

Computer Security logo

Computer Security

Anomaly detection, user-behavior analysis, malware analysis, and novel threat discovery.

Applied Mathematics logo

Applied Mathematics

Ultra-fast solving extra-large partial differential equations, high-dimensional integrals, and integro-differential equations.

Dynamic Networks and Ranking logo

Dynamic Networks and Ranking

Detection of latent communities in directed and undirected graphs and networks, ranking of latent research communities hidden in temporal multilayer networks.

Biology logo

Biology

Latent patterns in genomics, transcriptomics, metabolomics, proteomics, and cell membranes.

Material Science logo

Material Science

Analysis of combinatorial material libraries based on their: X-ray, Hyperspectral X-ray Imaging, Raman fluresence and other spectra.

Medicine logo

Medicine

Latent patterns in medical research.

Chemistry logo

Chemistry

Discoring new chemical pathways and reactions, radioisotope characterization, phase seperation analysis in complex liquids, and co-polymers.

Data Compression logo

Data Compression

Compression of large images and videos (e.g. asteroid water impacts), scientific computer-generated data, and more.

Climate logo

Climate

Ice and water masses trainsient patterns, micro-climate patterns.

Relational Databases logo

Relational Databases

Boolean factorization analysis of categorical patterns.

Privacy logo

Privacy

Data privacy with federated learning, and recommender systems.

Economy logo

Economy

Macro-economy analyses, and marketing.

Agriculture logo

Agriculture

Estimating the role of water, salt, and fertilizer content on the yield.

Software

T-ELF logo

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.

More Information
pyCP-APR logo

pyCP-APR

pyCP-APR is a Python library for tensor decomposition and anomaly detection that is developed as part of the R&D 100 award wining SmartTensors project. It is designed for the fast analysis of large datasets by accelerating computation speed using GPUs.

More Information
pyDNMFk logo

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).

More Information
AdversarialTensors logo

AdversarialTensors

Tensors-based framework for adversarial robustness. Library implements a variety of tensor factorization methods for defending Artificeal intelligence (AI) models against adversarial attacks.

More Information
pyDNTNK logo

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.

More Information
cuda-pyDNMFk logo

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.

More Information
pyDRESCALk logo

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.

More Information
pyHNMFk logo

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.

More Information
pyQBTNs logo

pyQBTNs

pyQBTNs is a Python library for boolean matrix and tensor factorization using D-Wave quantum annealers.

More Information