581 datasets found

A synthetic dataset for sceneries in the passenger compartment of ten different vehicles, to analyze machine learning-based approaches for their generalization capacities and...

The Diabetes dataset contains 10 variables-dimensions for a sample size (number of points) of 442 and a target (label) variable which quantifies diabetes progress.

The dataset used for machine learning accelerated discovery of porous organic cages.

The dataset used for machine learning accelerated discovery of metal-organic frameworks.

The dataset used for machine learning accelerated discovery of transition-metal complexes.

The dataset used in the paper is a collection of data points from the Grassmann manifold.

The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is...

ClimSim is a large multi-scale dataset for hybrid physics-ML climate emulation. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and...

Vision models with high overall accuracy often exhibit systematic errors on some important subsets of data, posing potential serious safety concerns. Diagnosing such bugs of...

The dataset used in this paper is a collection of T-cell receptor (TCR) repertoires from COVID-19 patients and healthy controls. The dataset is used to train and evaluate the...

The California Housing Dataset is a dataset containing information about housing prices in California, with nine features and a target variable of median house price.

Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only...

The dataset consists of spin configurations at various temperatures, including both higher and lower than the critical temperature.

The dataset used in the paper is a non-convex optimization problem, specifically the Rastrigin function and the Griewank function.

The dataset used in this paper is a multiclass classification task where the goal is to correctly predict which of the 7 activities is being performed by the user. The...

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

The dataset is used for modeling soft-failure evolution for triggering timely repair with low QoT margins.