Search Results for author:
Found 7 papers, 3 papers with code
High Speed Cognitive Domain Ontologies for Asset Allocation Using Loihi Spiking Neurons
In this work we show that a grid of isolated spiking neurons is capable of generating solutions to this problem very quickly, although some degree of approximation is required to achieve the speedup.
Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches.
Microscopic Nuclei Classification, Segmentation and Detection with improved Deep Convolutional Neural Network (DCNN) Approaches
The experimental results show that the proposed DCNN models provide superior performance compared to the existing approaches for nuclei classification, segmentation, and detection tasks.
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
Deep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System
IBM's 2016 release of a deep learning framework for DCNNs, called Energy Efficient Deep Neuromorphic Networks (Eedn).
Improved Inception-Residual Convolutional Neural Network for Object Recognition
In this paper, we introduce a new DCNN model called the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), which utilizes the power of the Recurrent Convolutional Neural Network (RCNN), the Inception network, and the Residual network.
Inception Recurrent Convolutional Neural Network for Object Recognition
Furthermore, we have investigated IRCNN performance against equivalent Inception Networks and Inception-Residual Networks using the CIFAR-100 dataset.
