487 datasets found

Photoacoustic tomography (PAT) datasets for neuroimaging with deep learning

Photoacoustic tomography (PAT) datasets for neuroimaging with 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.

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.

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...

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.

Prioritized Sequence Experience Replay (PSER) is a novel framework for prioritizing sequences of transitions to both learn more efficiently and effectively.

The dataset used in this paper is a simple dynamic neural network, which is a type of deep learning model. It is used to demonstrate the capabilities of the DeepLearning.scala...

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.

The dataset used in the paper is a set of molecular systems for which the authors investigate the convergence of deep variational Monte Carlo (VMC) approaches to the fixed-node...

A dataset of dataflow graphs for device placement, including Inception-v3, AmoebaNet, Transformer-XL, and WaveNet.

The proposed method uses the MNIST and CIFAR10 datasets for fully-connected DNNs.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.

The SWIN-Transformer model is used for traffic map prediction.

The dataset used in the paper is a binary classification dataset, where the goal is to find a hypothesis (i.e., a ResNet used in this work) f : X → Y such that f (x; Θ)...

Liver steatosis segmentation dataset with deep learning methods

A novel 3D deep learning network is proposed for brain MR image segmentation with randomized connection, which can decrease the dependency between layers and increase the...