samples = gumbel_distribution_sampling(n, loc, scale) gumbel max trick公式里就用到了这个采样思想,即先用均匀分布采样出一个随机值,然后把这个值带入到gumbel分布的CDF函数的逆函数(inverse function,或者称为反函数)得到采样值。另外值得一说的是,gumbel max trick里使用的gumbel分布是标准gumbel分布,即\mu=0...
上图只有一个stochastic units就是epsilon了,这个时候我们可以无阻碍地differentiate整个架构w.r.t. Phi和w(而不需要differentiate w.r.t. sampling,现在sampling只是一个outside procedure,用来给网络添加噪声) 二.Gumbel-Max/softmax 在深度学习中,很多时候我们想对离散数据进行采样。例如, 生成对抗网络(GAN)生成文...
The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval. To sample a random element $i$ in proportion to its positive weight...
今年的NIPS上的一篇Oral文章A* Sampling,把Gumbel-Max trick推广到连续空间,从概率理论里找来Gumbel process,然后提出了一种采样方法结合了Gumbel process的特点和A*搜索,给出了一种从连续分布中采样的方法。这个方法和adaptive rejection sampling很像,有些情况下更优。 总结:Gumbel-Max trick有时候有点用,但总的来...
diversitydeep-learningpytorchstochasticaccuracysamplingmanifoldmultimodalityvariational-inferencelikelihoodcvaegcngaussian-distributionvariational-autoencodergumbel-softmaxdiversehinge-losshuman-motion-predictionacmmm2022 UpdatedFeb 25, 2023 Python rtst777/TextGAN ...
diversitydeep-learningpytorchstochasticaccuracysamplingmanifoldmultimodalityvariational-inferencelikelihoodcvaegcngaussian-distributionvariational-autoencodergumbel-softmaxdiversehinge-losshuman-motion-predictionacmmm2022 UpdatedFeb 25, 2023 Python rtst777/TextGAN ...
chainer.functions.gumbel_softmax(log_pi,tau=0.1,axis=1)[source]¶ Gumbel-Softmax sampling function. This function draws samplesyiyifrom Gumbel-Softmax distribution, yi=exp((gi+logπi)/τ)∑jexp((gj+logπj)/τ),yi=exp((gi+logπi)/τ)∑jexp((gj+logπ...
(p1,p2,…,pK), because the unitary element will appear on theithelement in the one-hot vector with probabilitypi; therefore, the computation of Gumbel-softmax function can simulate the sampling process. Furthermore, this technique allows us to pass gradients directly through the “sampling” ...
By sampling from the Gumbel-Softmax distribution, an approximate sample from categorical distribution is generated so that the gradient-based optimizer can be used to train GAN on discrete data. In addition, we also design a strategy to dynamically adjust the temperature in the training process, ...
Thus, the vector of discrete probabilities associated with IGR is 𝔼[h(z~)], which can be easily approximated through a Monte Carlo estimate by sampling from the IGR and averaging the results after transforming them with h. This is the last cost to pay for losing parameter interpret...