Using limited assets, an interdictor attempts to destroy parts of a capacitated network through which an adversary will subsequently maximize flow. We formulate and solve a stochastic version of the interdictor’s problem: Minimize the expected maximum flow through the network when interdiction successes are binary random variables. Extensions are made to handle uncertain arc capacities and othe...

High dimensional sequential data exhibits complex structure, a successful generative model for such data must involve highly dependent, structured variables. Thus it is desired or even necessary to model correlations and dependencies between the multiple input, output variables and latent variables in such scenario. To achieve this goal, we introduce Variational Structured Stochastic Network(VS...

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Substantial work has applied stochastic techniques to network reliability models. These techniques can estimate risk, variance and uncertainty values. Unfortunately, these models do not address the issues of revenues, return on investment, or the time-value of money. To address these issues, we have developed an Economic Reliability Analysis [ERA] framework at the University of Virginia that fu...

Stochastic neural networks for global optimization are usually built by introducing random uctuations into the network. A natural method is to use stochastic weights rather than stochastic activation functions. We propose a new model in which each neuron has very simple functionality but all the weights are stochastic. It is shown that the stationary distribution of the network uniquely exists ...