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]: Principal Filter Analysis (PFA) is an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprint. We propose two compression algorithms: the first allows a user to specify the proportion of the original spectral energy that should be preserved in each layer after compression, while the second is a heuristic that leads to a parameter-free approach that automatically selects the compression used at each layer. Both algorithms are evaluated against several architectures and datasets, and we show considerable compression rates without compromising accuracy, e.g., for VGG-16 on CIFAR-10, CIFAR-100 and ImageNet, PFA achieves a compression rate of 8x, 3x, and 1.4x with an accuracy gain of 0.4%, 1.4% points, and 2.4% respectively. In our tests we also demonstrate that networks compressed with PFA achieve an accuracy that is very close to the empirical upper bound for a given compression ratio. Finally, we show how PFA is an effective tool for simultaneous compression and domain adaptation.
[EX A]: We propose an easy to implement, yet effective method for neural network compression. PFA exploits the intrinsic correlation between filter responses within network layers to recommend a smaller network footprints.

[EX Q]: Reinforcement learning provides a powerful and general framework for decision
making and control, but its application in practice is often hindered by the need
for extensive feature and reward engineering. Deep reinforcement learning methods
can remove the need for explicit engineering of policy or value features, but
still require a manually specified reward function. Inverse reinforcement learning
holds the promise of automatic reward acquisition, but has proven exceptionally
difficult to apply to large, high-dimensional problems with unknown dynamics. In
this work, we propose AIRL, a practical and scalable inverse reinforcement learning
algorithm based on an adversarial reward learning formulation that is competitive
with direct imitation learning algorithms. Additionally, we show that AIRL is
able to recover portable reward functions that are robust to changes in dynamics,
enabling us to learn policies even under significant variation in the environment
seen during training.
[EX A]:
We propose an adversarial inverse reinforcement learning algorithm capable of learning reward functions which can transfer to new, unseen environments.