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

Capturing high-level structure in audio waveforms is challenging because a single second of audio spans tens of thousands of timesteps.   While long-range dependencies are difficult to model directly in the time domain, we show that they can be more tractably modelled in two-dimensional time-frequency representations such as spectrograms.   By leveraging this representational advantage, in conjunction with a highly expressive probabilistic model and a multiscale generation procedure, we design a model capable of generating high-fidelity audio samples which capture structure at timescales which time-domain models have yet to achieve.   We demonstrate that our model captures longer-range dependencies than time-domain models such as WaveNet across a diverse set of unconditional generation tasks, including single-speaker speech generation, multi-speaker speech generation, and music generation.
We introduce an autoregressive generative model for spectrograms and demonstrate applications to speech and music generation

Deep neural networks and decision trees operate on largely separate paradigms; typically, the former performs representation learning with pre-specified architectures, while the latter is characterised by learning hierarchies over pre-specified features with data-driven architectures. We unite the two via adaptive neural trees (ANTs), a model that incorporates representation learning into edges, routing functions and leaf nodes of a decision tree, along with a backpropagation-based training algorithm that adaptively grows the architecture from primitive modules (e.g., convolutional layers). ANTs allow increased interpretability via hierarchical clustering, e.g., learning meaningful class associations, such as separating natural vs. man-made objects. We demonstrate this on classification and regression tasks, achieving over 99% and 90% accuracy on the MNIST and CIFAR-10 datasets, and outperforming standard neural networks, random forests and gradient boosted trees on the SARCOS dataset. Furthermore, ANT optimisation naturally adapts the architecture to the size and complexity of the training data.

We propose a framework to combine decision trees and neural networks, and show on image classification tasks that it enjoys the complementary benefits of the two approaches, while addressing the limitations of prior work.