GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Diederik P. Kingma, Prafulla Dhariwal: Glow: Generative Flow with Invertible 1x1 Convolutions, NeurIPS, 2018 Neural Ordinary Differential Equations, NeurIPS, 2018 Masked Autoregressive Flow for Density Estimation, NeurIPS, 2017 5.2 参考代码: https://github.com/simonwestber github.com/S-razmi/Real_NV...
复现结果,代码可参考https://github.com/linzangsc/reproduction-of-NICE.git 写在最后 流模型有着漂亮的理论,足够深的coupling layer能够去拟合任何分布。但流模型也存在一些问题导致它并没有被用的那么多,其中一个问题是学习到的 x=f(z) 可能由于比较陡峭导致梯度很大,随机梯度下降的过程中有可能会出现nan,所以...
The source code is available at https://github.com/Rehgar3/AttnFlow .Chenyu WangLin MaHanlin QinShuowen YangRuiyun LiXiaotao ShiNeurocomputing
TTTFlow: Unsupervised Test-Time Training with Normalizing Flow David Osowiechi* , Gustavo A. Vargas Hakim*, Mehrdad Noori , Milad Cheraghalikhani , Ismail Ben Ayed , and Christian Desrosiers LIVIA, E´ TS Montre´al, Canada International Laboratory on Learning ...
Flow++还采用了类似Transformer的网络结构，每个块由卷积和注意力机制组成（公式16），详细的代码可参阅GitHub上的开源项目。文章实验展示了在图像生成上的SOTA结果，显示了在非自回归模型中的卓越表现，但与自回归模型仍存在差距。通过消融研究，文章分析了去量化、混合对数CDF和注意力块在模型性能提升中的...
By simultaneously predicting the flow parameters and the Poisson mean, it is possible to model the correlation between the two. The input to the MLP are the event parameter labels z_o and the module position in (x,y) coordinates. 1.4 Parameter choices We choose a single-layer GRU with ...
A permutation can transform a simple random variable into a complex random variable for greater expressiveness, called a normalizing flow. We then define scoring functions by measuring the similarity of two normalizing flows, namely NFE. We construct several instantiating models and prove that they ...
Simulation from the variational conditional flow then amends to solving an equality constraint. Our contribution is three-fold: a) we provide detailed insights on the choice of variational distributions; b) we discuss how to partition the input space of the flow to preserve bijectivity property; ...