Recent years have witnessed the promise that reinforcement learning, coupled
with Graph Neural Network (GNN) architectures, could learn to solve hard
combinatorial optimization problems: given raw input data and an evaluator to
guide the process, the idea is to automatically learn a policy able to return
feasible and high-quality outputs. Recent work have shown promising results but
the latter were mainly evaluated on the travelling salesman problem (TSP) and
similar abstract variants such as Split Delivery Vehicle Routing Problem
(SDVRP). In this paper, we analyze how and whether recent neural architectures
can be applied to graph problems of practical importance. We thus set out to
systematically "transfer" these architectures to the Power and Channel
Allocation Problem (PCAP), which has practical relevance for, e.g., radio
resource allocation in wireless networks. Our experimental results suggest that
existing architectures (i) are still incapable of capturing graph structural
features and (ii) are not suitable for problems where the actions on the graph
change the graph attributes. On a positive note, we show that augmenting the
structural representation of problems with Distance Encoding is a promising
step towards the still-ambitious goal of learning multi-purpose autonomous
solvers.

本文研究如何将深度强化学习和图神经网络应用于无线网络中的电力和信道分配问题，研究结果表明现有结构还不能很好地识别图的结构和特征，并且对于对图产生影响的问题不太适用，但研究还是取得了一些积极进展，比如通过距离编码来增强问题的表示方法。

超越 TSP 的神经组合优化：现有架构在图结构下表现不足

Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure

Binaural audio gives the listener the feeling of being in the recording place
and enhances the immersive experience if coupled with AR/VR. But the problem
with binaural audio recording is that it requires a specialized setup which is
not possible to fabricate within handheld devices as compared to traditional
mono audio that can be recorded with a single microphone. In order to overcome
this drawback, prior works have tried to uplift the mono recorded audio to
binaural audio as a post processing step conditioning on the visual input. But
all the prior approaches missed other most important information required for
the task, i.e. distance of different sound producing objects from the recording
setup. In this work, we argue that the depth map of the scene can act as a
proxy for encoding distance information of objects in the scene and show that
adding depth features along with image features improves the performance both
qualitatively and quantitatively. We propose a novel encoder-decoder
architecture, where we use a hierarchical attention mechanism to encode the
image and depth feature extracted from individual transformer backbone, with
audio features at each layer of the decoder.

该研究旨在提高在手持设备上录制立体声音频的难度，通过借助深度图像等特征来实现对距离信息的编码，从而改善其在 AR/VR 等环境下的表现。