Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting This is a PyTorch implementation of Diffusion Convolutional Recurrent Neural Network in the following paper: Yaguang Li, Rose Yu, Cyrus Shahabi, Yan Liu,Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Fore...
Pytorch implementation of the Variational Recurrent Neural Network (VRNN). - emited/VariationalRecurrentNeuralNetwork
Quasi-Recurrent Neural Network (QRNN) for PyTorch 项目地址:salesforce/pytorch-qrnn 这个项目包含一个 PyTorch 实现的 Salesforce Research's Quasi-Recurrent Neural Networks 论文. This repository contains a PyTorch implementation of Salesforce Research's Quasi-Recurrent Neural Networks paper. The QRNN provid...
Bayesian-Neural-Network-Pytorch This is a lightweight repository of bayesian neural network for PyTorch. Usage 📋 Dependencies torch 1.2.0 python 3.6 🔨 Installation pip install torchbnn or git clone https://github.com/Harry24k/bayesian-neural-network-pytorch import torchbnn 🚀 Demos Bayesian ...
注:当使用kaiming_normal或kaiming_normal_进行初始化时,应使用nonlinearity='linear'而不是nonlinearity='selu',以获得自归一化神经网络。 更多细节详见Self-Normalizing Neural Networks一文。 (2)SELU函数表达式及图像 逐元素执行: 式中:α=1.6732632423543772848170429916717,scale=1.0507009873554804934193349852946 ...
更多资料:Some important Pytorch tasks 利用Siamese network 来解决one-shot learning:https://sorenbouma.github.io/blog/oneshot/译文:【深度神经网络 One-shot Learning】孪生网络少样本精准分类 A PyTorch Implementation of "Siamese Neural Networks for One-shot Image Recognition"...
For more information please refer to the paper "Weight Uncertainty in Neural Networks" and get deeper into the theory behind it! The implementation in this paper is inspired by several other implementations of the same idea, specially the one in https://www.nitarshan.com/bayes-by-backprop/....
对于 PyTorch 模型,Open Neural Network Exchange (ONNX)格式起到了这样的作用。 我们可能希望将我们的模型集成到更大的应用程序中。为此,如果我们不受 Python 的限制将会很方便。因此,我们将探讨使用 PyTorch 模型从 C++中使用的想法,这也是通往任何语言的一个过渡。 最后,对于一些像我们在第二章中看到的图像斑马...
Theinclude aand a walkthrough of, a modern reinforcement learning model. There’s also a wonderfully comprehensivefrom Stanford’s Justin Johnson, while theinclude—among other things—a deep convolutional generative adversarial network (DCGAN) and models for ImageNet andneural machine translation. Rich...
xLSTM的新闻大家可能前几天都已经看过了,原作者提出更强的xLSTM,可以将LSTM扩展到数十亿参数规模,我们今天就来将其与原始的lstm进行一个详细的对比,然后再使用Pytorch实现一个简单的xLSTM。 xLSTM xLSTM 是对传统 LSTM 的一种扩展,它通过引入新的门控机制和记忆结构来改进 LSTM,旨在提高 LSTM 在处理大规模数据...