动态专家混合对抗严重分布偏移
Dynamic Mixture of Experts Against Severe Distribution Shifts
AI总结：
本文提出动态专家混合方法，用于持续学习和强化学习环境，通过动态增加容量来解决分布偏移问题。
摘要：
构建能够持续学习并适应演变数据流的神经网络的挑战是持续学习（CL）和强化学习（RL）领域的核心问题。这个问题通常以可塑性-稳定性困境来阐述，关注诸如可塑性丧失和灾难性遗忘等问题。与神经网络不同，生物大脑通过容量增长维持可塑性，这促使研究者探索人工网络中的类似方法，例如动态增加容量。先前的解决方案往往缺乏参数效率或依赖于显式任务索引，但专家混合（MoE）架构通过为不同的分布专门化专家提供了有希望的替代方案。本文旨在评估动态MoE方法在持续和强化学习环境中的有效性，并将其与现有网络扩展方法进行基准测试。
英文摘要：
The challenge of building neural networks that can continuously learn and adapt to evolving data streams is central to the fields of continual learning (CL) and reinforcement learning (RL). This lifelong learning problem is often framed in terms of the plasticity-stability dilemma, focusing on issues like loss of plasticity and catastrophic forgetting. Unlike neural networks, biological brains maintain plasticity through capacity growth, inspiring researchers to explore similar approaches in artificial networks, such as adding capacity dynamically. Prior solutions often lack parameter efficiency or depend on explicit task indices, but Mixture-of-Experts (MoE) architectures offer a promising alternative by specializing experts for distinct distributions. This paper aims to evaluate a DynamicMoE approach for continual and reinforcement learning environments and benchmark its effectiveness against existing network expansion methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
