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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. -
Hybrid ELB-NN for accuracy and computational complexity tradeoffs
Hybrid ELB-NN for accuracy and computational complexity tradeoffs. Our experimental results indicate that the accuracy varies with the precisions of weights and activations with... -
Child Growth Monitor Dataset
A dataset of depth images collected from children under 5 years of age using a smartphone, used for height estimation. -
Zoom and Learn
The dataset used for zoom and learn, a method for generalizing deep stereo matching. -
Breaking the Deadly Triad with a Target Network
The dataset used in the paper "Breaking the Deadly Triad with a Target Network" for training and testing the proposed algorithms. -
A Mathematical Motivation for Complex-valued Convolutional Networks
A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an... -
Litter detection with deep learning: A comparative study
The dataset used for litter detection with deep learning: A comparative study. -
TensorQuant
TensorQuant toolbox is used to apply fixed point quantization to DNNs. The simulations are focused on popular CNN topologies, such as Inception V1, Inception V3, ResNet 50 and...
