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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,... -
CLABSI prediction using random forests
The dataset used in this study is a large electronic health record (EHR) dataset containing information on hospital admissions, catheter episodes, and patient outcomes. -
Best-scored Random Forest
The dataset used in this paper is a binary classification problem. -
Extracting Rules from Event Data for Study Planning
Event data from Campus Management Systems (CMS) to analyze the study paths of higher education students. -
Statistical guarantees for stochastic Metropolis-Hastings
The dataset is used for statistical guarantees for stochastic Metropolis-Hastings in nonparametric regression. -
FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Desig...
A large dataset for automatic design flow parameter tuning, consisting of 9 designs with 1728 parameter samples each. -
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... -
Tensor Kernel Recovery for Spatio-Temporal Hawkes Processes
The dataset is used to estimate the general influence functions for spatio-temporal Hawkes processes using a tensor recovery approach. -
50 Classification Data Sets
The dataset used in the paper is a collection of 50 classification data sets downloaded from the UCI Machine Learning Repository. -
Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning
Ageing analysis of embedded SRAM on a large-scale testbed using machine learning -
Graph-based Molecular Representation Learning
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. -
MEKA: A multi-label/multi-target extension to Weka
MEKA is a multi-label/multi-target extension to Weka. -
scikit-multilearn: A scikit-based Python environment for performing multi-lab...
scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all... -
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... -
Identification of closure terms using machine learning
The dataset used in this paper is a collection of kinetic simulations of magnetic reconnection. The simulations are performed using the iPiC3D code and are used to train machine... -
Gradient Adversarial Training
The dataset used for gradient adversarial training of neural networks. -
Sinusoidal functions dataset
The dataset used in this paper is a collection of sinusoidal functions with different dimensions. -
CCAIR-AI-Reproducibility
The dataset contains citation contexts from papers involved in Machine Learning Reproducibility Challenges. -
LEARNING MIXTURES OF LINEAR CLASSIFIERS
The dataset used in the paper is a mixture of linear classifiers, where each component corresponds to a generalified linear model with parameter vector uℓ ∈ Rd.
