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Training Data and Runtime Monitoring for Safety Critical ML Applications
The dataset used in the study on challenges encountered when specifying training data and runtime monitors for safety critical ML applications. -
Quantum Neural Networks
The dataset used in this paper is a collection of quantum neural network models, including VQA, CV, swap test and phase estimation, RUS, quantum generalization, QBM, QCVNN,... -
Best-scored Random Forest
The dataset used in this paper is a binary classification problem. -
LoRa-based Soil Moisture Sensor Dataset
The dataset used in this paper for soil humidity estimation based on signal strength measurement. -
Adversarial Data Programming: Using GANs to Relax the Bottleneck of Curated L...
Paucity of large curated hand-labeled training data for every domain-of-interest forms a major bottleneck in the deployment of machine learning models in computer vision and... -
Blind Justice: Fairness with Encrypted Sensitive Attributes
The dataset used in the paper is not explicitly described, but it is mentioned that it contains sensitive attributes such as gender or race. -
Scatterbrained: A flexible and expandable pattern for decentralized machine l...
A flexible framework for decentralized machine learning -
MAQA: A Quantum Framework for Supervised Learning
The Multiple Aggregator Quantum Algorithm (MAQA) is a quantum framework that can reproduce the output of a plethora of classical supervised machine learning algorithms using... -
Ensemble Transform Kalman Filter (ETKF) for Data Assimilation
The dataset used in this paper is a set of synthetic data for the 3-variable Lorenz system and for the Kuramoto-Sivashinsky system, simulating model error in each case by a... -
Gradient Adversarial Training
The dataset used for gradient adversarial training of neural networks. -
HELOC Dataset
The HELOC dataset is a multivariate dataset containing information about home credit applications. It includes variables such as external risk estimate, MSinceOldestTradeOpen,... -
Machine Learning Based Routing Congestion Prediction in FPGA High-Level Synth...
A machine-learning based method to predict routing congestion for FPGA high-level synthesis -
FOLD-TR: A Scalable and Efficient Inductive Learning Algorithm for Learning t...
FOLD-TR is a customized FOLD-R++ algorithm with ranking framework, that aims to rank new items following the ranking pattern in the training data. -
Spectral Graph Wavelet Transform as Feature Extractor for Machine Learning in...
Graph Signal Processing has become a very useful framework for signal operations and representations defined on irregular domains. Exploiting transformations that are defined on... -
Iterative Retraining Dataset
The dataset used for the iterative retraining experiments, which includes 20% augmented training and validation sets. -
TaxiNet Dataset
The dataset used for the TaxiNet case study, which includes 6-dimensional semantic feature space defined by SCENIC programs and searched by VERIFAI. -
Fashion-MNIST, CIFAR-10, and GTSRB datasets
The Fashion-MNIST, CIFAR-10, and GTSRB datasets were used to evaluate differentiable logics for learning systems. -
LtU-ILI dataset
The dataset used in this paper is a collection of simulated data for various astrophysics and cosmology problems. -
BRITE Light Curves
The BRITE light curves were used as the training samples of non-transit light curves. The transit signals were injected into the BRITE light curves to produce synthetic transit...
