-
Deep Learning Hamiltonian Monte Carlo
The dataset is used for simulating lattice gauge theories, specifically 2D U(1) gauge theory, and evaluating the efficiency of the Deep Learning Hamiltonian Monte Carlo algorithm. -
A deep learning framework for assessing physical rehabilitation exercises
A deep learning framework for assessing physical rehabilitation exercises. -
ModelOps-based Framework for Intelligent Medical Knowledge Extraction
A ModelOps-based framework for intelligent medical knowledge extraction -
Deep Frame Prediction for Video Coding
The proposed DNN-based frame prediction architecture that is able to support both uni- and bi-directional prediction. -
Gaussian Denoising Dataset
The dataset used for Gaussian denoising, created by augmenting and frequency-manipulating images to tap into the epistemic uncertainty of a pretrained denoiser. -
Morse Neural Networks for Uncertainty Quantification
The Morse neural network is a deep generative model useful for uncertainty quantification. -
Wafer Dicing Dataset
The dataset used in this paper for visual attention and deep learning for wafer dicing and defect detection. -
Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm
Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses,... -
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control. -
SAH dataset
The dataset is composed of a consecutive series of patients admitted to our hospital with a confirmed diagnosis of aneurysmal subarachnoid hemorrhage (SAH) between 2016 and 2022. -
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Au...
High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time in clinical settings, thus restricting the use of downstream analysis techniques that... -
MixupE: Understanding and Improving Mixup from Directional Derivative Perspec...
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels. -
FashionMNIST, CIFAR10, CIFAR100, and STL10
The dataset used in the paper is a collection of images from FashionMNIST, CIFAR10, CIFAR100, and STL10 datasets. -
Deepfashion
Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. -
Breast Cancer
A neural network with single-hidden layer of 64 hidden units and ReLU activations. A prior precision of ε = 1, a minibatch size of 128 and 16 Monte-Carlo samples are used for... -
CIFAR10, CIFAR100, SVHN, ImageNet
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used four widely used datasets: CIFAR10, CIFAR100, SVHN, and ImageNet. -
MCA: Moment Channel Attention Networks
Channel attention mechanisms endeavor to recalibrate channel weights to enhance representation abilities of networks. -
SleepPoseNet
The dataset used in this study for sleep postural transition (SPT) recognition using ultra-wideband (UWB) radar.
