In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: We show that information about whether a neural network's output will be correct or incorrect is present in the outputs of the network's intermediate layers. To demonstrate this effect, we train a new "meta" network to predict from either the final output of the underlying "base" network or the output of one of the base network's intermediate layers whether the base network will be correct or incorrect for a particular input. We find that, over a wide range of tasks and base networks, the meta network can achieve accuracies ranging from 65% - 85% in making this determination.

[EX A]: Information about whether a neural network's output will be correct or incorrect is somewhat present in the outputs of the network's intermediate layers.

[EX Q]: We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets in lifelong learning scenarios on multiple public datasets, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch model with substantially fewer number of parameters.
[EX A]: We propose a novel deep network architecture that can dynamically decide its network capacity as it trains on a lifelong learning scenario.

[EX Q]: Recent work on explanation generation for decision-making problems has viewed the explanation process as one of model reconciliation where an AI agent brings the human mental model (of its capabilities, beliefs, and goals) to the same page with regards to a task at hand. This formulation succinctly captures many possible types of explanations, as well as explicitly addresses the various properties -- e.g. the social aspects, contrastiveness, and selectiveness -- of explanations studied in social sciences among human-human interactions. However, it turns out that the same process can be hijacked into producing "alternative explanations" -- i.e. explanations that are not true but still satisfy all the properties of a proper explanation. In previous work, we have looked at how such explanations may be perceived by the human in the loop and alluded to one possible way of generating them. In this paper, we go into more details of this curious feature of the model reconciliation process and discuss similar implications to the overall notion of explainable decision-making.
[EX A]:
Model Reconciliation is an established framework for plan explanations, but can be easily hijacked to produce lies.