computation of the second order delay in rc-tree based circuits is important during the design process of modern vlsi systems with respect to having tree structure circuits. calculation of the second and higher order moments is possible in tree based networks. because of the closed form solution, computation speed and the ease of using the performance optimization in vlsi design methods such as...

We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample via a single feedforward pass through a multilayer preceptron, as in the recently proposed generative adversarial networks (Goodfellow et al., 2014). Training a generative adversarial network, however, requires careful optimization of a difficult minimax program. Inst...

Models of stochastic image deformation allow study time-continuous effects transforming images by deforming the domain. Applications include longitudinal medical analysis with both population trends and random subject-specific variation. Focusing on a extension LDDMM models evolutions governed EPDiff equation, we use moment approximations corresponding Itô diffusion to construct estimators for ...

We give a new framework for proving the existence of low-degree, polynomial approximators for Boolean functions with respect to broad classes of non-product distributions. Our proofs use techniques related to the classical moment problem and deviate significantly from known Fourier-based methods, which require the underlying distribution to have some product structure. Our main application is t...

in this paper, a new method is investigated for model order reduction of high order systems based on moment matching technique. in this method, at first, full order model is expanded by legendre wavelet function which is included in orthogonal functions. a suitable fixed structure model is considered as reduced order model whose parameters are unknown. these unknown parameters are determined us...

We consider the problem of learning a set probability distributions from empirical Bellman dynamics in distributional reinforcement (RL), class state-of-the-art methods that estimate distribution, as opposed to only expectation, total return. formulate method learns finite statistics each return distribution via neural networks, RL literature. Existing however constrain learned predefined funct...

In this paper we study the problem of model reduction by moment matching for stochastic systems. We characterize mathematical object which generalizes notion to differential equations and find a class models achieve matching. However, differently from deterministic case, these reduced-order cannot be considered "simpler" because high computational cost paid determine moment. To overcome difficu...