32 datasets found

The dataset used in this paper for efficient industrial refrigeration scheduling with peak pricing.

The dataset is used for energy-cost aware scheduling. The goal is to schedule tasks on machines to minimize energy consumption.

The dataset used in the paper is a multivariate normal distribution with a mean of zero and standard deviation of one for each covariate ζi and travel time ξ(i,j) with a...

The dataset used to test the proposed new mutation and evaluation function.

The Maximin Latin Hypercube Designs dataset is used to test the proposed new mutation and evaluation function.

The dataset used in the paper is the Traveling Salesman Problem (TSP) instances, specifically the Burma'7 instance, and six additional instances with 5 to 14 nodes.

The dataset used in the paper is not explicitly described, but it is mentioned that the authors used various optimization algorithms to solve complex problems.

The dataset used in the paper is the MATPOWER dataset.

The dataset used in the paper is a synthetic dataset for prescriptive learning, which contains features and outcomes for a binary treatment.

The dataset used in the paper is the MCNC benchmark, which consists of 5 problems: apte, xerox, hp, ami33, and ami49. Each problem has a different number of macros and requires...

The benchmark test-suite is a collection of twelve well-known test functions from the literature, including unimodal, multimodal, separable, and non-separable functions. The...

The dataset used in this paper for optimization methods for MR image reconstruction

The (cid:96)1 ball constrained optimization problem.

A dataset for testing the proposed algorithm, consisting of a road network with 1719 vertices and 2280 edges.

The dataset used in the paper is a collection of 30 test functions with 30 and 50 dimensions, used for evaluating the performance of the Adaptive Coordinate System (ACoS)...

DaCeML is a Data-Centric Machine Learning framework that provides a simple, flexible, and customizable pipeline for optimizing training of arbitrary deep neural networks.

The dataset used in this paper is a set of 10 city TSP samples with different parameters.

The dataset used in the paper is a set of images and videos of objects moving in different environments, used for training and testing the Motion Planning Diffusion model.

This dataset is used to test the robustness of watermarking methods against adaptive attacks.

The dataset used in the paper is not explicitly described, but it is mentioned that it is a class of regression models GN.