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:
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features. Recently, there has been an increasing interest in extending CNNs to the general spatial domain. Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood. In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation. Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains.

Ex Output:
We devise a novel Depthwise Separable Graph Convolution (DSGC) for the generic spatial domain data, which is highly compatible with depthwise separable convolution.

Ex Input:
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e. the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with an auxiliary task for state prediction and best practices for multi-agent learning, SAD establishes a new state of the art for 2-5 players on the self-play part of the Hanabi challenge.

Ex Output:
We develop Simplified Action Decoder, a simple MARL algorithm that beats previous SOTA on Hanabi by a big margin across 2- to 5-player games.

Ex Input:
Residual and skip connections play an important role in many current
generative models. Although their theoretical and numerical advantages
are understood, their role in speech enhancement systems has not been
investigated so far. When performing spectral speech enhancement,
residual connections are very similar in nature to spectral subtraction,
which is the one of the most commonly employed speech enhancement approaches.
Highway networks, on the other hand, can be seen as a combination of spectral
masking and spectral subtraction. However, when using deep neural networks, such operations would
normally happen in a transformed spectral domain, as opposed to traditional speech
enhancement where all operations are often done directly on the spectrum.
In this paper, we aim to investigate the role of residual and highway
connections in deep neural networks for speech enhancement, and verify whether
or not they operate similarly to their traditional, digital signal processing
counterparts. We visualize the outputs of such connections, projected back to
the spectral domain, in models trained for speech denoising, and show that while
skip connections do not necessarily improve performance with regards to the
number of parameters, they make speech enhancement models more interpretable.

Ex Output:
We show how using skip connections can make speech enhancement models more interpretable, as it makes them use similar mechanisms that have been explored in the DSP literature.