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Human-level control through deep reinforcement learning
The dataset contains data from human-level control through deep reinforcement learning. -
VGG-16 and ResNet-50 DNNs
The VGG-16 and ResNet-50 DNNs are used as victim DNNs in the attack. -
VGG and ResNet DNNs
The VGG and ResNet DNNs are used as victim DNNs in the attack. -
ICML archive
The dataset used in the paper is the ICML archive, which contains papers and code from the ICML conferences. -
NeurIPS archive
The dataset used in the paper is the NeurIPS archive, which contains papers and code from the NeurIPS conferences. -
Tensor Regression Networks with various Low-Rank Tensor Approximations
Tensor regression networks achieve high compression rate of neural networks while having slight impact on performances. They do so by imposing low tensor rank structure on the... -
Self-Supervised MRI Reconstruction with Unrolled Diffusion Models
Magnetic Resonance Imaging (MRI) produces excellent soft tissue contrast, albeit it is an inherently slow imaging modality. Promis- ing deep learning methods have recently been... -
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches t...
A deep learning model that learns subject-level representation from a set of local features. The model represents the image volume as a bag (or set) of local features and can... -
Waste Classification using Computer Vision and Deep Learning
Dataset for waste classification using computer vision and deep learning -
Deep Neural Networks
Deep Neural Networks (DNNs) are universal function approximators providing state-of-the-art solutions on wide range of applications. Common perceptual tasks such as speech... -
Virtual Adversarial Training
The authors used MNIST, SVHN, and CIFAR10 datasets for their experiments. -
TPMIC T2-FLAIR dataset
Local T2-FLAIR dataset used for testing the performance of the proposed Brain Slice Classification Algorithm (BSCA). -
ADNI T2-FLAIR dataset
T2-weighted fluid-attenuated-inversion-recovery (T2-FLAIR) magnetic resonance imaging (MRI) dataset used for training and testing a deep-learning-based model to automatically... -
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.
