Meta-learning usually refers to a learning algorithm that learns from other
learning algorithms. The problem of uncertainty in the predictions of neural
networks shows that the world is only partially predictable and a learned
neural network cannot generalize to its ever-changing surrounding environments.
Therefore, the question is how a predictive model can represent multiple
predictions simultaneously. We aim to provide a fundamental understanding of
learning to learn in the contents of Decentralized Neural Networks
(Decentralized NNs) and we believe this is one of the most important questions
and prerequisites to building an autonomous intelligence machine. To this end,
we shall demonstrate several pieces of evidence for tackling the problems above
with Meta Learning in Decentralized NNs. In particular, we will present three
different approaches to building such a decentralized learning system: (1)
learning from many replica neural networks, (2) building the hierarchy of
neural networks for different functions, and (3) leveraging different modality
experts to learn cross-modal representations.

本研究致力于通过元学习算法解决神经网络在面对不确定性时难以适应环境变化的问题，并探讨如何利用去中心化神经网络 (Decentralized Neural Networks, Decentralized NNs) 在解决多重预测问题、构建层次化神经网络模型以及建立跨模态预测模型等方面实现自主智能机器的梦想。

分散式神經網絡中的元學習：走向更為通用的 AI

Meta Learning in Decentralized Neural Networks: Towards More General AI

This paper studies online algorithms augmented with multiple machine-learned
predictions. While online algorithms augmented with a single prediction have
been extensively studied in recent years, the literature for the multiple
predictions setting is sparse. In this paper, we give a generic algorithmic
framework for online covering problems with multiple predictions that obtains
an online solution that is competitive against the performance of the best
predictor. Our algorithm incorporates the use of predictions in the classic
potential-based analysis of online algorithms. We apply our algorithmic
framework to solve classical problems such as online set cover, (weighted)
caching, and online facility location in the multiple predictions setting. Our
algorithm can also be robustified, i.e., the algorithm can be simultaneously
made competitive against the best prediction and the performance of the best
online algorithm (without prediction).

本文研究了在多个机器学习预测的基础上增强的在线算法。我们提出了一个通用的算法框架，用于多重预测的在线覆盖问题，该算法能够获得与最佳预测器性能相竞争的在线解决方案。该算法还能够同时使竞争性达到最佳预测和最佳在线算法的性能水平，并应用于解决一些经典问题。