In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Here, we propose a brain-inspired winner-take-all emotional neural network (WTAENN) and prove the universal approximation property for the novel architecture. WTAENN is a single layered feedforward neural network that benefits from the excitatory, inhibitory, and expandatory neural connections as well as the winner-take-all (WTA) competitions in the human brain’s nervous system. The WTA competition increases the information capacity of the model without adding hidden neurons. The universal approximation capability of the proposed architecture is illustrated on two example functions, trained by a genetic algorithm, and then applied to several competing recent and benchmark problems such as in curve fitting, pattern recognition, classification and prediction. In particular, it is tested on twelve UCI classification datasets, a facial recognition problem, three real world prediction problems (2 chaotic time series of geomagnetic activity indices and wind farm power generation data), two synthetic case studies with constant and nonconstant noise variance as well as k-selector and linear programming problems. Results indicate the general applicability and often superiority of the approach in terms of higher accuracy and lower model complexity, especially where low computational complexity is
[EX A]: A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property

[EX Q]: In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.
[EX A]: Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

[EX Q]: Recent years have seen the development of a number<lb>of methods for multiagent planning under uncertainty<lb>that scale to tens or even hundreds of agents. However,<lb>most of these methods either make restrictive assump-<lb>tions on the problem domain, or provide approximate<lb>solutions without any guarantees on quality. To allow<lb>for meaningful benchmarking through measurable qual-<lb>ity guarantees on a very general class of problems, this<lb>paper introduces a family of influence-optimistic upper<lb>bounds for factored Dec-POMDPs. Intuitively, we de-<lb>rive bounds on very large multiagent planning problems<lb>by subdividing them in sub-problems, and at each of<lb>these sub-problems making optimistic assumptions with<lb>respect to the influence that will be exerted by the rest<lb>of the system. We numerically compare the different<lb>upper bounds and demonstrate how, for the first time<lb>ever, we can achieve a non-trivial guarantee that the<lb>heuristic solution of problems with hundreds of agents<lb>is close to optimal. Furthermore, we provide evidence<lb>that the upper bounds may improve the effectiveness of<lb>heuristic influence search, and discuss further potential<lb>applications to multiagent planning.
[EX A]:
Influence-Optimistic Local Values for Multiagent Planning — Extended Version