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 Input:
Inspired by neurophysiological discoveries of navigation cells in the mammalian
brain, we introduce the first deep neural network architecture for modeling Egocentric
Spatial Memory (ESM). It learns to estimate the pose of the agent and
progressively construct top-down 2D global maps from egocentric views in a spatially
extended environment. During the exploration, our proposed ESM network
model updates belief of the global map based on local observations using a recurrent
neural network. It also augments the local mapping with a novel external
memory to encode and store latent representations of the visited places based on
their corresponding locations in the egocentric coordinate. This enables the agents
to perform loop closure and mapping correction. This work contributes in the
following aspects: first, our proposed ESM network provides an accurate mapping
ability which is vitally important for embodied agents to navigate to goal locations.
In the experiments, we demonstrate the functionalities of the ESM network in
random walks in complicated 3D mazes by comparing with several competitive
baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)
algorithms. Secondly, we faithfully hypothesize the functionality and the working
mechanism of navigation cells in the brain. Comprehensive analysis of our model
suggests the essential role of individual modules in our proposed architecture and
demonstrates efficiency of communications among these modules. We hope this
work would advance research in the collaboration and communications over both
fields of computer science and computational neuroscience.

Ex Output:
first deep neural network for modeling Egocentric Spatial Memory inspired by neurophysiological discoveries of navigation cells in mammalian brain

Ex Input:
In most current formulations of adversarial training, the discriminators can be expressed as single-input operators, that is, the mapping they define is separable over observations. In this work, we argue that this property might help explain the infamous mode collapse phenomenon in adversarially-trained generative models. Inspired by discrepancy measures and two-sample tests between probability distributions, we propose distributional adversaries that operate on samples, i.e., on sets of multiple points drawn from a distribution, rather than on single observations. We show how they can be easily implemented on top of existing models. Various experimental results show that generators trained in combination with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with observation-wise prediction discriminators. In addition, the application of our framework to domain adaptation results in strong improvement over recent state-of-the-art.

Ex Output:
We show that the mode collapse problem in GANs may be explained by a lack of information sharing between observations in a training batch, and propose a distribution-based framework for globally sharing information between gradients that leads to more stable and effective adversarial training.

Ex Input:
Deep learning models have outperformed traditional methods in many fields such
as natural language processing and computer vision. However, despite their
tremendous success, the methods of designing optimal Convolutional Neural Networks
(CNNs) are still based on heuristics or grid search. The resulting networks
obtained using these techniques are often overparametrized with huge computational
and memory requirements. This paper focuses on a structured, explainable
approach towards optimal model design that maximizes accuracy while keeping
computational costs tractable. We propose a single-shot analysis of a trained CNN
that uses Principal Component Analysis (PCA) to determine the number of filters
that are doing significant transformations per layer, without the need for retraining.
It can be interpreted as identifying the dimensionality of the hypothesis space
under consideration. The proposed technique also helps estimate an optimal number
of layers by looking at the expansion of dimensions as the model gets deeper.
This analysis can be used to design an optimal structure of a given network on
a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate
these techniques by optimizing VGG and AlexNet networks on CIFAR-10,
CIFAR-100 and ImageNet datasets.

Ex Output:
We present a single shot analysis of a trained neural network to remove redundancy and identify optimal network structure