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Found 15 papers, 1 papers with code
Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization
One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text.
Interactive Distillation of Large Single-Topic Corpora of Scientific Papers
Given a small initial "core" corpus of papers, we build a citation network of documents.
MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware
Here we introduce and showcase preliminary capabilities of a new method that can perform precise identification of novel malware families, while also unifying the capability for malware/benign-ware classification and malware family classification into a single framework.
SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection
As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents.
FedSPLIT: One-Shot Federated Recommendation System Based on Non-negative Joint Matrix Factorization and Knowledge Distillation
Non-negative matrix factorization (NMF) with missing-value completion is a well-known effective Collaborative Filtering (CF) method used to provide personalized user recommendations.
Topic Analysis of Superconductivity Literature by Semantic Non-negative Matrix Factorization
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 topics.
COVID-19 Multidimensional Kaggle Literature Organization
The unprecedented outbreak of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, continues to be a significant worldwide problem.
Boolean Hierarchical Tucker Networks on Quantum Annealers
Quantum annealing is an emerging technology with the potential to solve some of the computational challenges that remain unresolved as we approach an era beyond Moore's Law.
Quantum Physics
Identification of Anomalous Diffusion Sources by Unsupervised Learning
Fractional Brownian motion (fBm) is a ubiquitous diffusion process in which the memory effects of the stochastic transport result in the mean squared particle displacement following a power law, $\langle {\Delta r}^2 \rangle \sim t^{\alpha}$, where the diffusion exponent $\alpha$ characterizes whether the transport is subdiffusive, ($\alpha<1$), diffusive ($\alpha = 1$), or superdiffusive, ($\alpha >1$).
A Neural Network for Determination of Latent Dimensionality in Nonnegative Matrix Factorization
An important input for NMF is the latent dimensionality of the data, that is, the number of hidden features, K, present in the explored data set.
Determination of Latent Dimensionality in International Trade Flow
Nonnegative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities.
Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix Factorization Integrated with Custom Clustering
Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.
Nonnegative/binary matrix factorization with a D-Wave quantum annealer
Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method.
Identification of release sources in advection-diffusion system by machine learning combined with Green function inverse method
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.
Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals
Especially difficult is the case when the number of sources of the signals with delays is unknown and has to be determined from the data as well.
