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UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Class...
Convolutional neural network for sentiment analysis -
CifarQuick
The CifarQuick model is a medium-sized convolutional neural network trained on the Cifar-10 dataset. -
RepBNN: towards a precise Binary Neural Network with Enhanced Feature Map via...
Binary neural network (BNN) is an extreme quantization version of convolutional neural networks (CNNs) with all features and weights mapped to just 1-bit. -
SqueezeJet: High-level Synthesis Accelerator
Deep convolutional neural networks have dominated the pattern recognition scene by providing much more accurate solutions in computer vision problems such as object recognition... -
Broadband DOA Estimation Using Convolutional Neural Networks Trained with Noi...
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... -
Optimally Scheduling CNN Convolutions for Efficient Memory Access
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. -
Minimal Filtering Algorithms for Convolutional Neural Networks
The dataset used in this paper is a set of convolutional neural networks with small length FIR filters. -
Frequency-Aware Re-Parameterization for Over-Fitting Based Image Compression
Over-fitting based image compression requires weights compactness for compression and fast convergence for practical use, posing challenges for deep convolutional neural... -
Quantum CNN Dataset
The dataset used in this paper is a dataset for training quantum convolutional neural networks. -
Quantum CNN
The dataset used in this paper is a quantum convolutional neural network (QCNN) dataset. -
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control. -
Sorting of Smartphone Components for Recycling Through Convolutional Neural N...
A dataset with 1,127 images of pyrolyzed smartphone components, used to train and assess a VGG-16 image classification model for sorting waste electrical and electronic... -
FPGA deep learning acceleration based on convolutional neural network
This paper proposes a convolutional neural network hardware accelerator based on field programmable logic gate array (FPGA). -
Convnet-benchmarks
The dataset used in this paper is a benchmark suite for Convolutional Neural Networks. -
Caffe Framework with FPGA Support
The dataset used in this paper is a modified version of the Caffe CNN framework with support for FPGA implementations.
