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Medical Knowledge-Guided Deep Curriculum Learning for Elbow Fracture Diagnosi...
Elbow fracture diagnosis from X-ray images using medical knowledge-guided deep curriculum learning -
GSE: Group-wise Sparse and Explainable Adversarial Attacks
Sparse adversarial attacks fool deep neural networks (DNNs) through minimal pixel perturbations, often regularized by the ℓ0 norm. Recent efforts have replaced this norm with a... -
ShiftAddViT: Towards Efficient Vision Transformers
ShiftAddViT: A hardware-inspired multiplication-reduced Vision Transformer model. -
Kaggle Dataset
The dataset used in the paper is a publicly available dataset from Kaggle, used for demonstrating the effectiveness of the Lai loss function. -
Deep k-NN for Noisy Labels
The dataset used in the paper is not explicitly described, but it is mentioned that the authors used a preliminary model to compute an intermediate representation and then... -
Genetic Algorithm based hyper-parameters optimization for transfer Convolutio...
Hyperparameter optimization for transfer Convolutional Neural Networks (CNN) using Genetic Algorithm -
Machine Learning and Deep Learning Methods for Cybersecurity
Machine learning and deep learning methods for cybersecurity -
Density Estimation Using Real NVP
This dataset has no description
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Alternating Back-Propagation for Generator Networks
This dataset has no description
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Wasserstein GAN
This dataset has no description
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Flexible Prior Distributions for Deep Generative Models
The dataset induced prior distribution is learned using a secondary GAN named PGAN. This prior is then used to further train the original GAN. -
Reconfigurable Intelligent Surface-assisted Classification of Modulations using...
The dataset includes five digital modulations that are used in modern communication systems: BPSK, QPSK, 8-PSK, 16-QAM and 64-QAM. -
Max-Margin Deep Generative Models
Deep generative models (DGMs) are effective on learning multilayered represen- tations of complex data and performing inference of input data by exploring the generative ability. -
DeepSNR: A deep learning foundation for offline gravitational wave detection
The DeepSNR detection pipeline uses a novel method for generating an SNR ranking statistic from deep learning classifiers, providing for the first time a foundation for powerful... -
Spinor Field Networks
The dataset used in the paper is a collection of point clouds with spinor features, where each point cloud is associated with a spinor feature and a regression target. -
Radnet: Radiologist Level Accuracy Using Deep Learning for Hemorrhage Detecti...
A dataset of studies tagged slice-wise by radiologists for training a deep learning algorithm for detection of hemorrhage in CT scans. -
Deep 3D Convolution Neural Network for CT Brain Hemorrhage Classification
A dataset of 40k studies assembled for training a 3D convolution neural network for CT brain hemorrhage classification. -
Improved ICH Classification Using Task-Dependent Learning
BloodNet is a deep learning architecture designed for optimal triaging of Head CTs, with the goal of decreasing the time from CT acquisition to accurate ICH detection. -
Norm-based Generalization Bounds for Compositionally Sparse Neural Networks
The dataset used in this paper is a multilayered sparse neural network, specifically a convolutional neural network.
