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Data provided by
National Institute of Standards and Technology
This repository contains results for experiments on generative modeling of synthetic Orthogonal-Frequency Division Multiplexing (OFDM) communication signals. (This record supersedes Software and Data for Modeling OFDM Communication Signals with Generative Adversarial Networks, formerly at
https://doi.org/10.18434/mds2-2428)
Tags: generative adversarial network,machine learning,wireless communications,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 360 QA AI models using a small set of model architectures.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 360 QA AI models using a small set of model architectures.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.
Tags: Trojan Detection,Artificial Intelligence,ai,machine learning,Adversarial Machine Learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).
Tags: wireless coexistence,Spectrum sharing,machine learning,wireless communications and networks,4G,5G,6G,Wi-Fi,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This project produces synthetic datasets of spectrum sharing simulation results (I/Q data, metadata, and KPIs).
Tags: wireless coexistence,Spectrum sharing,machine learning,wireless communications and networks,4G,5G,6G,Wi-Fi,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
A code repository and accompanying data for incorporating imperfect theory into machine learning for improved prediction and explainability. Specifically, it focuses on the case study of the dimensions of a polymer chain in different solvent qualities. Jupyter Notebooks for quickly testing concepts and reproducing figures, as well as source code that computes the mean squared error as a function of dataset size for various machine learning models are included.For additional details on the data, please refer to the README.md associated with the data.
Tags: polymers,machine learning,transfer learning,theory,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
SolDet is an object-oriented package for solitonic feature detection in absorption images of Bose-Einstein condensate. with wider use for cold atom image analysis. Featured with classifier, object detector, and Mexican hat metric methods. Technical details are explained in
https://arxiv.org/abs/2111.04881.
Tags: soliton,machine learning,python package,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
This data set consists of both measured and simulated optical intensities scattered off periodic line arrays, with simulations based upon an average geometric model for these lines. These data were generated in order to determine the average feature sizes based on optical scattering, which is an inverse problem for which solutions to the forward problem are calculated using electromagnetic simulations after a parameterization of the feature geometry.
Tags: electromagnetic simulations,simulations,experimental,angle-resolved scattering,scattering,gratings,patterned semiconductors,semiconductors,scatterfield microscopy,bright-field microscopy,microscopy,inverse problems,machine learning,
Modified: 2022-07-29
Views: 0
Data provided by
National Institute of Standards and Technology
The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization.
Tags: machine learning,data analysis,data processing,materials science,materials genome initiative,
Modified: 2022-07-29
Views: 0
