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.

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

Neural networks with low-precision weights and activations offer compelling
 efficiency advantages over their full-precision equivalents. The two most
 frequently discussed benefits of quantization are reduced memory consumption,
 and a faster forward pass when implemented with efficient bitwise
 operations. We propose a third benefit of very low-precision neural networks:
 improved robustness against some adversarial attacks, and in the worst case,
 performance that is on par with full-precision models. We focus on the very
 low-precision case where weights and activations are both quantized to $\pm$1,
 and note that stochastically quantizing weights in just one layer can sharply
 reduce the impact of iterative attacks. We observe that non-scaled binary neural
 networks exhibit a similar effect to the original \emph{defensive distillation}
 procedure that led to \emph{gradient masking}, and a false notion of security.
 We address this by conducting both black-box and white-box experiments with
 binary models that do not artificially mask gradients.
We conduct adversarial attacks against binarized neural networks and show that we reduce the impact of the strongest attacks, while maintaining comparable accuracy in a black-box setting

Dense word vectors have proven their values in many downstream NLP tasks over the past few years. However, the dimensions of such embeddings are not easily interpretable. Out of the d-dimensions in a word vector, we would not be able to understand what high or low values mean. Previous approaches addressing this issue have mainly focused on either training sparse/non-negative constrained word embeddings, or post-processing standard pre-trained word embeddings. On the other hand, we analyze conventional word embeddings trained with Singular Value Decomposition, and reveal similar interpretability. We use a novel eigenvector analysis method inspired from Random Matrix Theory and show that semantically coherent groups not only form in the row space, but also the column space. This allows us to view individual word vector dimensions as human-interpretable semantic features.

Without requiring any constraints or post-processing, we show that the salient dimensions of word vectors can be interpreted as semantic features. 

