However, most systems of practical interest are non-linear, requiring more complex models. Many approximate inference methods have been developed for non-linear dynamical systems: Deterministic methods approximate the filtering and smoothing distributions e.g. by using a Taylor series

Proof A.1 W e firstly give Lemma 1: Lemma 1 The operation weights are caculated by a softmax function. The number of target node's intra-group neighbors is "S" indicates the setting of searching phase. "E" indicates the setting of evaluation phase. The hyper-parameter λ which controls the hidden feature smoothness is set to be 0 .125 . We show the variance of synthetic graph experiment in Table 1 to endorse our analysis in Section 3. The table shows that the variance of accuracy is relatively big in the experiment setting. However, all the results are average of 100 runs.

We first introduce Equivari-ant Stein V ariational Gradient Descent algorithm - an equivariant sampling method based on Stein's identity for sampling from densities with symmetries.

Markov decision process (MDP) is a paradigm for modeling sequential decision making under uncertainty. From a modeling perspective, some parameters of MDPs are unknown and need to be estimated from data.

How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty.

We study the role of the representation of state-action value functions in regret minimization in finite-horizon Markov Decision Processes (MDPs) with linear structure.