50 datasets found

Convolutional neural network for sentiment analysis

The CifarQuick model is a medium-sized convolutional neural network trained on the Cifar-10 dataset.

Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit.

Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition...

A convolution neural network (CNN) based classification method for broadband DOA estimation is proposed, where the phase com-ponent of the short-time Fourier transform...

The dataset used in this paper is a discretised neutron diffusion equation, solved using a convolutional neural network with pre-determined weights.

We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment.

The dataset used in the paper is a set of physical field observations with noisy data.

The dataset used in this paper is a CNN convolutional layer loop-nest, which is a 6-level loop-nest representing the convolutional layer of a CNN.

The dataset used in this paper is a convolution kernel dataset, which is used to train and evaluate the MetaTune cost model.

The dataset used in the paper is not explicitly mentioned, but it is implied to be a CNN model on CIFAR-10 and ImageNet.

The dataset used in this paper is a set of convolutional neural networks with small length FIR filters.

Over-fitting based image compression requires weights compactness for compression and fast convergence for practical use, posing challenges for deep convolutional neural...

The proposed method, called MixNet, mixes depthwise separable convolutions.

The proposed method, called EfficientNet, rethinks model scaling for convolutional neural networks.

The AUC Distracted Driver Dataset was used to evaluate the proposed approach.

The dataset used in this paper is a collection of state-of-the-art convolutional primitives for ARM Cortex-M microcontrollers.

The dataset used in this paper is a dataset for training quantum convolutional neural networks.

The dataset used in this paper is a quantum convolutional neural network (QCNN) dataset.

Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control.