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Lookahead Pruning
The dataset used in this paper is a neural network, and the authors used it to test the performance of their lookahead pruning method. -
MobileNetV2
The dataset used in this paper is a MobileNetV2 model, which is a type of deep neural network. The dataset is used to evaluate the performance of the proposed heterogeneous system. -
LUT-NN: Empower Efficient Neural Network Inference with Centroid Learning and...
The dataset used in the paper is not explicitly described. However, it is mentioned that the authors used a range of datasets, including CIFAR-10, GTSRB, Google Speech Command,... -
Validation and Testing Datasets
A validation dataset and a testing dataset were created using the remaining image volumes from 70 subjects. -
Training Dataset
The training dataset is a collection of the publicly available Arabic corpora listed below: The unshuffled OSCAR corpus (Ortiz Su´arez et al., 2020). The Arabic Wikipedia dump... -
Atari Learning Environment
The dataset used in this paper is the Atari Learning Environment (ALE) dataset, which consists of 15 Atari video games. -
CTR Prediction Models
The dataset used in this paper is a real-world online sponsor advertising application, containing user click history logs from Baidu’s search engine. -
Mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet
The dataset used in the paper is a mixture of Gaussians, CIFAR-10, STL-10, CelebA, and ImageNet. -
Interpretable Multi-Task Deep Neural Networks for Dynamic Predictions of Post...
A large retrospective cohort of 43,943 adult patients undergoing 52,529 major inpatient surgeries. -
HeteroEdge
The dataset used in the paper is a testbed comprising two Unmanned Ground Vehicle (UGVs) and two NVIDIA Jetson devices. -
MB-DECTNet: A Model-Based Unrolled Network for Accurate 3D DECT Reconstruction
The dataset used in the paper is a set of dual-energy CT sinograms and reconstructed images. -
Various Datasets
The datasets used in the paper are described as follows: WikiMIA, BookMIA, Temporal Wiki, Temporal arXiv, ArXiv-1 month, Multi-Webdata, LAION-MI, Gutenberg. -
A Deep Generative Model of Speech Complex Spectrograms
This paper proposes an approach to the joint modeling of the short-time Fourier transform magnitude and phase spectrograms with a deep generative model. -
WRN28x10 dataset
The dataset used in this paper is the WRN28x10 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets. -
VGG16 dataset
The dataset used in this paper is the VGG16 dataset, a deep neural network trained on the CIFAR-10 and CIFAR-100 datasets. -
ResNet50 dataset
The dataset used in this paper is the ResNet50 dataset, a deep neural network trained on the ImageNet dataset. -
INFOBATCH: LOSSLESS TRAINING SPEED UP BY UNBIASED DYNAMIC DATA PRUNING
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. -
COVID-19 Segmentation from CT Images
The dataset used for COVID-19 segmentation from CT images, using deep learning and imaging for delineating COVID-19 infection in lungs. -
Exploring the Limits of Large Scale Pre-training
A dataset for exploring the limits of large-scale pre-training.
