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Technique used in stochastic gradient variational inference
The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational
Reparameterization_trick
Deep learning generative model to encode data representation
networks are typically trained together with the usage of the reparameterization trick, although the variance of the noise model can be learned separately
Variational_autoencoder
Particular case of the generalized extreme value distribution
distribution. This technique is called "Gumbel-max trick" and is a special example of "reparameterization tricks". In detail, let ( π 1 , … , π n ) {\displaystyle
Gumbel_distribution
Concept in decision-making
\mu _{W},\Sigma _{W},\mu _{b},\Sigma _{b}} are learned via the reparameterization trick. Berger-Tal, Oded; Nathan, Jonathan; Meron, Ehud; Saltz, David
Exploration–exploitation dilemma
Exploration–exploitation_dilemma
Class of reinforcement learning algorithms
}(A_{j}|S_{j})\cdot \Psi _{i}|S_{i}=s_{i}]=0.} Proofs Proof of the lemma Use the reparameterization trick. E π θ [ ∇ θ ln π θ ( A j | S j ) | S i = s i ] = ∑ s P r ( S
Policy_gradient_method
Statistical model used in machine learning
are fixed functions that define the autoregressive model. By the reparameterization trick, the autoregressive model is generalized to a normalizing flow:
Flow-based_generative_model
Generative adversarial network variant
\nabla _{\theta }\ln \rho _{\mu _{G}}(x)]} where we used the reparameterization trick. As shown, the generator in GAN is motivated to let its μ G {\displaystyle
Wasserstein_GAN
Paradigm in machine learning that uses no classification labels
Sampling, and backpropagating reconstruction errors or hidden state reparameterizations. See the table below for more details. An energy function is a macroscopic
Unsupervised_learning
\}=W^{s}(p_{0})\cap W^{u}(p_{0})\cup \{p_{0}\}.} To obtain the Melnikov function, some tricks have to be used, for example, to get rid of the time dependence and to gain
Melnikov_distance
Motion of a curve based on its curvature
to move along the curve, as the curve evolves. Choosing a careful reparameterization can help redistribute the vertices more evenly along the curve in
Curve-shortening_flow
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