It takes advantage of the efficiency of unidirectional recurrent networksandtheeffectiveness ofsliding-window-based methods.

Circuits based on sum-product structure have become a ubiquitous representation to compactly encode knowledge, from Boolean functions to probability distributions. By imposing constraints on the structure of such circuits, certain inference queries become tractable, such as model counting and most probable configuration. Recent works have explored analyzing probabilistic and causal inference queriesas compositions of basic operators to derive tractability conditions. In this paper, we take an algebraic perspective for compositional inference, and show that a large class of queries--including marginal MAP, probabilistic answer set programming inference, and causal backdoor adjustment--correspond to a combination of basic operators over semirings: aggregation, product, and elementwise mapping. Using this framework, we uncover simple and general sufficient conditions for tractable composition of these operators, in terms of circuit properties (e.g., marginal determinism, compatibility) and conditions on the elementwise mappings. Applying our analysis, we derive novel tractability conditions for many such compositional queries. Our results unify tractability conditions for existing problems on circuits, while providing a blueprint for analysing novel compositional inference queries.

This paper studies the problem of real-world video super-resolution (VSR) for animation videos, and reveals three key improvements for practical animation VSR. First, recent real-world super-resolution approaches typically rely on degradation simulation using basic operators without any learning capability, such as blur, noise, and compression. In this work, we propose to learn such basic operators from real low-quality animation videos, and incorporate the learned ones into the degradation generation pipeline. Such neural-network-based basic operators could help to better capture the distribution of real degradations. Second, a large-scale high-quality animation video dataset, AVC, is built to facilitate comprehensive training and evaluations for animation VSR. Third, we further investigate an efficient multi-scale network structure. It takes advantage of the efficiency of unidirectional recurrent networks and the effectiveness of sliding-window-based methods. Thanks to the above delicate designs, our method, AnimeSR, is capable of restoring real-world low-quality animation videos effectively and efficiently, achieving superior performance to previous state-of-the-art methods.

Circuits based on sum-product structure have become a ubiquitous representation to compactly encode knowledge, from Boolean functions to probability distributions. By imposing constraints on the structure of such circuits, certain inference queries become tractable, such as model counting and most probable configuration. Recent works have explored analyzing probabilistic and causal inference queriesas compositions of basic operators to derive tractability conditions. In this paper, we take an algebraic perspective for compositional inference, and show that a large class of queries--including marginal MAP, probabilistic answer set programming inference, and causal backdoor adjustment--correspond to a combination of basic operators over semirings: aggregation, product, and elementwise mapping. Using this framework, we uncover simple and general sufficient conditions for tractable composition of these operators, in terms of circuit properties (e.g., marginal determinism, compatibility) and conditions on the elementwise mappings.

Summary: Lifted inference enables tractable inference in large probabilistic models by exploiting symmetries and independencies over variables. This paper provides a comprehensive method to (a) specify constraints; (b) use them for lifted inference; and (c) produce constraints from evidence. The authors first introduce setineq a constraint language for specifying symmetries and independencies using set membership and (in)equality constraints, and supply basic operators for manipulating these constraints. Next, The authors develop algorithms to use these constraints and basic operators to produce lifting rules (Decomposer/Binomial). Finally, the authors present a greedy method for deriving constraints from evidence.

Swift for TensorFlow was introduced by Chris Lattner at TensorFlow Dev Summit 2018. On April 27, 2018 Google team has made its first release to public community on their GitHub repository. But Swift for TensorFlow is still in its infancy stage. And it seems to be too early for developers/researchers to use it in their projects. If you are still interested in trying it out then install Swift for TensorFlow's snapshot from Swift Official website.