deep learning
2021年9月13日 You are right in a sense that it is better to be called log of unnormalized probability. This way, the quantity could be positive or negative. For ex...
...for Monte Carlo with unnormalized probability distr...
Infinite order automatic differentiation for Monte Carlo expectations from unnormalized probability distributions. Introduction Due to the nature of Metropolis-Ha...
Softmax与Sigmoid函数的联系
为什么这些未正则化概率值是求和得到(影响是加性的)?(Why is the unnormalized probability a summation?) 我们理所当然的认为canonical线性组合的语义是 。但是为什么先求和...
论文阅读笔记(四十六):Generative Adversarial Nets
2018年5月12日 (such as DBNs and DBMs), it is not even possible to derive a tractable unnormalized probability density. Some models such as denoising auto-encoders ...
Something
2017年5月14日 Keeping in mind that any positive tally can be viewed as an unnormalized probability density function , one approach to this problem is to estimate t...
BDA3 Chapter 1 Probability and inference
2020年2月13日 2.probability VS likelihood Pr(data|distribution); L(distribution|data); The likelihood function is unnormalized probability distribution describing ...
[arxiv 2014]GAN
2018年9月10日 Note that in many interesting generative models with several layers of latent variables (such as DBNs and DBMs), it is not even possible to derive a ...
A Neural Network MCMC sampler that maximizes Proposal ...
Markov Chain Monte Carlo (MCMC) methods sample from unnormalized probability distributions and offer guarantees of exact sampling. However, in the continuous case...
TensorFlow 2.0深度强化学习指南
2019年1月28日 (1, name='value')# logits are unnormalized log probabilitiesself.logits = kl.Dense(num_actions, name='policy_logits') self.dist = ProbabilityDistribution()defcall(self, ...
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