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Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep...
The proposed attack is based on transfer-based and score-based methods and is both gradient-free and query-efficient. -
Occluded CIFAR
The dataset used in the paper is Occluded CIFAR. -
Cluttered MNIST and CIFAR-10
The dataset used in the paper is Cluttered MNIST and CIFAR-10. -
The CIFAR-10 dataset is used to evaluate the performance of FracBNN.
The CIFAR-10 dataset is used to evaluate the performance of FracBNN. -
The ImageNet dataset is used to evaluate the performance of FracBNN.
The ImageNet dataset is used to evaluate the performance of FracBNN. -
The first convolutional layer is not binarized.
The first convolutional layer is not binarized. -
Binary neural networks (BNNs) have 1-bit weights and activations.
Binary neural networks (BNNs) have 1-bit weights and activations. -
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional A...
FracBNN: Accurate and FPGA-Efficient Binary Neural Networks with Fractional Activations -
CIFAR10 and ImageNet
The dataset used in the paper to evaluate the alignment of deep neural networks with human perception. -
Deep Image: Scaling up image recognition
Deep Image: Scaling up image recognition -
Batch Normalization: Accelerating Deep Network Training by Reducing Internal ...
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. -
Hybrid Quantum-Classical Neural Network with Deep Residual Learning
A hybrid quantum-classical neural network with deep residual learning to improve the performance of cost function for deeper networks. -
ATCN: Agile Temporal Convolutional Network
This paper presents a scalable deep learning model called Agile Temporal Convolutional Network (ATCN) for high-accurate fast classification and time series prediction in... -
Churn analysis using deep convolutional neural networks and autoencoders
Customer temporal behavioral data represented as images to perform churn prediction by leveraging deep learning architectures prominent in image classification. -
CNN Models
The dataset used in this paper is a large variety of popular CNN models, such as straight-forward, complicated-connected, and grouped architectures. -
PoseAction: Action Recognition for Patients in the Ward using Deep Learning A...
Real-time intelligent detection and prediction of subjects' behavior particularly their movements or actions is critical in the ward. -
CarSpeedNet: A Deep Neural Network-based Car Speed Estimation from Smartphone...
A comprehensive dataset of acceleration signals from multiple smartphones, collected from cars navigating through various regions in Israel. -
Im2win: An Efficient Convolution Paradigm on GPU
Convolutional neural network (CNN) is an important network model widely used in computer vision, image processing, and scientific computing. CNN consists of an input layer, an... -
Australia's long-term electricity demand forecasting using deep neural networks
Monthly electricity consumption data for Australia, including socio-economic and environmental factors.
