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Anomalous diffusion dynamics of learning in deep neural networks
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used ResNet-14, ResNet-20, ResNet-56, and ResNet-110 networks, as well as... -
MIDL 2019 – Extended Abstract Track: Uncertainty Quantification in Computer-A...
A dataset of optical coherence tomography scans showing four different retinal conditions. -
CNN Model Dataset
The dataset used in this paper is a dataset of four CNN models: ResNet-18, Vgg-16, Squeezenet v1.0, and AlexNet. -
Convolution Kernel Dataset
The dataset used in this paper is a convolution kernel dataset, which is used to train and evaluate the MetaTune cost model. -
Accelerating Deep Learning with Shrinkage and Recall
Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and... -
Data-driven quantitative photoacoustic imaging using tissue-mimicking phantoms
A collection of tissue-mimicking phantoms for supervised training and evaluation of learned quantitative PAI methods on experimental data. -
COVID-19 Identification ResNet (CIdeR)
The COVID-19 Identification ResNet (CIdeR) dataset consists of 517 crowdsourced coughing and breathing audio recordings from 355 participants, of which 62 participants had tested... -
High-Fidelity Image Generation With Fewer Labels
High-fidelity image generation with fewer labels -
Swin Deformable Attention U-Net Transformer (SDAUT) for Explainable Fast MRI
Fast MRI aims to reconstruct a high fidelity image from partially observed measurements. Exuberant development in fast MRI using deep learning has been witnessed recently.... -
Convolutional LSTM network: A machine learning approach for precipitation now...
Convolutional LSTM network: A machine learning approach for precipitation nowcasting. -
Deep ensemble learning for segmenting tuberculosis-consistent manifestations ...
Automated segmentation of tuberculosis (TB)-consistent lesions in chest X-rays (CXRs) using deep learning (DL) methods can help reduce radiologist effort, supplement clinical... -
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic
DSAC-C: Constrained Maximum Entropy for Robust Discrete Soft-Actor Critic -
Fine-tuning of WMn network for CSFn-MPRAGE images
A fine-tuning approach was employed to incorporate information learned from the WMn network to segment thalamic nuclei from CSFn-MPRAGE images, which have poor intra-thalamic... -
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutio...
A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization... -
Very Deep Convolutional Networks for Large-Scale Image Recognition
The dataset consists of 60,000 images of objects in 200 categories, with 300 images per category. -
Transformations between deep neural networks
The dataset used in the paper is a collection of neural networks trained on different tasks, including scalar functions, two-dimensional vector fields, and images of a rotating... -
Automated Segmentation of Vertebrae on Lateral Chest Radiography Using Deep L...
Automated segmentation of vertebrae on lateral chest radiography using deep learning -
Automatic detection and counting of wheat spikelet
A dataset used for automatic detection and counting of wheat spikelet using semi-automatic labeling and deep learning. -
Deep Learning Assisted Calibrated Beam Training for Millimeter-Wave Communica...
The dataset used in this paper is a collection of received signals of wide beam training and corresponding optimal narrow beam indices.
