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A deep learning framework for assessing physical rehabilitation exercises
A deep learning framework for assessing physical rehabilitation exercises. -
Morse Neural Networks for Uncertainty Quantification
The Morse neural network is a deep generative model useful for uncertainty quantification. -
Deep learning for ultrasound image formation: Cubdl evaluation framework and ...
Deep learning for ultrasound image formation: Cubdl evaluation framework and open datasets. -
Synthetic Vasculature Phantom Dataset
Photoacoustic tomography (PAT) datasets for neuroimaging with deep learning -
Lung Vasculature Dataset
Photoacoustic tomography (PAT) datasets for neuroimaging with deep learning -
Fundus Vasculature Dataset
Photoacoustic tomography (PAT) datasets for neuroimaging with deep learning -
Mouse Brain Vasculature Dataset
Photoacoustic tomography (PAT) datasets for neuroimaging with deep learning -
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. -
Prioritized Sequence Experience Replay
Prioritized Sequence Experience Replay (PSER) is a novel framework for prioritizing sequences of transitions to both learn more efficiently and effectively. -
Monadic Deep Learning
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... -
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. -
Convergence to the fixed-node limit in deep variational Monte Carlo
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... -
GDP: GENERALIZED DEVICE PLACEMENT
A dataset of dataflow graphs for device placement, including Inception-v3, AmoebaNet, Transformer-XL, and WaveNet. -
Alternating optimization method based on nonnegative matrix factorizations fo...
The proposed method uses the MNIST and CIFAR10 datasets for fully-connected DNNs. -
Part VI: combining compressions
Model compression is generally performed by using quantization, low-rank approximation or pruning, for which various algorithms have been researched in recent years.
