Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing paradigm. However, the typical shallow SNN architectures have limited capacity for expressing complex representations while training deep SNNs using input spikes has...
A simple tank battle game but it's controlled by Neural Network. (⭐ 220)
We have demonstrated neural networks can recognize parts by visual images. Input signals are gray scale photographs of objects consisting of some parts and output signals are their shapes. By training neural networks by a few set of images, without...
To characterize the function space explored by neural networks (NNs) is an important aspect of learning theory. In this work, noticing that a multi-layer NN generates implicitly a hierarchy of reproducing kernel Hilbert spaces (RKHSs) - named a neura...
This project contains code for a fully convolutional neural network (FCN) for semantic segmentation with a region of interest (ROI) map as an additional input (Figure 1). The net receives image and ROI as a binary map with pixels corresponding to ROI marked 1, and produce pixel-w…
A neural field models the large scale behaviour of large groups of neurons. We extend results of van Gils et al. [2013] and Dijkstra et al. [2015] by including a diffusion term into the neural field, which models direct, electrical connections. We ex...
This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture. (⭐ 2064)
Mar 8, 2018 · A convolutional neural network (CNN) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer.
Granger causality discovery for neural networks. (⭐ 235)
Tools to Design or Visualize Architecture of Neural Network (⭐ 5286)
Classifying malignant and benign tumors using Neural Networks ? (⭐ 18)
Reduced-precision arithmetic improves the size, cost, power and performance of neural networks in digital logic. In convolutional neural networks, the use of 1b weights can achieve state-of-the-art error rates while eliminating multiplication, reduci...
Predicting future stock price using wavelet denoising and neural network (Long-Short Term Memory) (⭐ 5)
Code Transformer neural network components piece by piece (⭐ 377)
Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand innovative neural architecture designs, particularly within the constrained environments of Internet of Things (IoT) systems, to balance performance and effic...
Training neural audio classifiers with few data − https://arxiv.org/abs/1810.10274 (⭐ 60)
Implicit neural representations (INRs) have gained prominence as a powerful paradigm in scene reconstruction and computer graphics, demonstrating remarkable results. By utilizing neural networks to parameterize data through implicit continuous functi...
Convolutional neural networks (CNNs) can now achieve human-level performance on challenging object recognition tasks. CNNs are also the leading quantitative models in terms of predicting neural and behavioral responses in visual recognition tasks. Ho...
Mar 8, 2018 · A convolutional neural network (CNN) is a neural network where one or more of the layers employs a convolution as the function applied to the output of the previous layer.
A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual...