487 datasets found

The dataset used in this paper is a real-world online sponsor advertising application, containing user click history logs from Baidu’s search engine.

The dataset used in the paper is a mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet.

A large retrospective cohort of 43,943 adult patients undergoing 52,529 major inpatient surgeries.

The dataset used in the paper is a testbed comprising two Unmanned Ground Vehicle (UGVs) and two NVIDIA Jetson devices.

The dataset used in the paper is a set of dual-energy CT sinograms and reconstructed images.

The datasets used in the paper are described as follows: WikiMIA, BookMIA, Temporal Wiki, Temporal arXiv, ArXiv-1 month, Multi-Webdata, LAION-MI, Gutenberg.

This paper proposes an approach to the joint modeling of the short-time Fourier transform magnitude and phase spectrograms with a deep generative model.

The dataset used in this paper is the WRN28x10 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets.

The dataset used in this paper is the VGG16 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets.

The dataset used in this paper is the ResNet50 dataset, a deep neural network trained on the ImageNet dataset.

Data pruning aims to obtain lossless performances with less overall cost. A common approach is to filter out samples that make less contribution to the training.

The dataset used for COVID-19 segmentation from CT images, using deep learning and imaging for delineating COVID-19 infection in lungs.

A dataset for exploring the limits of large-scale pre-training.

A smoothly broken power law functional form that accurately models and extrapolates the scaling behaviors of deep neural networks for various architectures and tasks.

A large-scale generated video benchmark dataset for network training and evaluation, comprising synthetic videos from 11 different generator models.

MIMIC-III is a large, publicly available clinical database containing information from the Medical Information Mart for Intensive Care III. It is used for various clinical...

The dataset used in the paper is a batch of data collected from a fixed batch of data which has already been gathered, without offering further possibility for data collection.

The proposed method uses the layers of a pretrained deep neural network as building blocks to construct an ML system that can jointly solve an arbitrary number of tasks.

The MNIST and CIFAR10 datasets are used to evaluate the Cycle-Consistent Adversarial GAN (CycleAdvGAN) for image classification.

NIH CXR database for lung segmentation, including CXR images with severe abnormal findings.