Deep Learning Travels
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Density Estimation Using Real NVP
WHY? The motivation is almost the same as that of NICE. This papaer suggest more elaborate transformation to represent complex data. WHAT? NICE suggested coupling layers with tractable Jacobian matrix. This paper suggest flexible bijective function while keeping the property of coupling layers. Affine coupling layers scale and translate the...

NICE: Nonlinear Independent Components Estimation
WHY? Modeling data with known probability distribution has a lot of advantages. We can exactly calculate the log likelihood of the data and easily sample new data from distribution. However, finding tractable transformation of data into probability distribution or vice versa is difficult. For instance, a neural encoder is a...

Categorical Reparameterization with GunbelSoftmax
WHY? The same motivation with Concrete. WHAT? GumbelSoftmax distribution is the same as Concrete distribution. GS distribution appoaches to onhot as temperature goes 0. However, GS samples are not exactly the same as categorical samples resulting bias. This GS estimator becomes close to unbiased but the variance of gradient increase...

Deterministic Policy Gradient Algorithms
WHY? Policy gradient usually requires integral over all the possible actions. WHAT? The purpose of reinforcement learning is to learn the policy to maximize the objective function. Policy gradient directly train the policy network to minimize the objective function. Stochastic Policy Gradient Since this assumes stochastic policy, this is called...

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
WHY? Reparameterization trick is a useful technique for estimating gradient for loss function with stochastic variables. While score function extimators suffer from great variance, RT enable the gradient to be estimated with pathwise derivatives. Even though reparameterization trick can be applied to various kinds of random variables enabling backpropagation, it...