Continual Metalearning

元学习，也被称为“学会学习”，研究技术和机器学习模型，能够快速适应和推广新的任务和环境，而不必每次都经历漫长的训练过程。持续学习侧重于开发能够处理连续数据流的方法。持续元学习的进步将极大地影响现代机器学习模型在工业、经济和社会中的应用，这些领域的数据通常是以连续流的方式接收的。为了将机器学习模型部署到现实世界中，它们需要能够检测模型/数据不匹配、概念漂移、异常和样本外量化。这些模型需要度量来量化性能下降的时间、方式和程度，以确定何时需要更新它们的参数。我们的目标是创建能够区分数据中有意义和无意义变化的模型，并适应前者。自适应模型需要动态架构，并且在处理顺序数据流时非常合适。在现实世界中，检测并快速适应有意义的变化对于模型预测保持可靠至关重要。目的和目标该项目的目的是推进持续元学习领域，使机器学习模型在工业环境中有意义的集成，并处理现实世界的数据。该项目的第一个任务是提供对现有技术的观察。特别是，我们将关注神经过程并将其扩展到持续学习领域。神经过程是混合模型，像高斯过程一样定义函数上的分布，并具有像神经网络一样的计算效率训练，具有适应数据的先验能力[Garnelo等人，2018b,a]。后续的研究可以在包含多领域和多领域的有意义归纳偏差，以及神经过程集成/存储的迁移学习能力方面进行。在哲学博士课程中，我们将探索动态模型的发展，这些模型具有检测模型数据不匹配、概念漂移、异常和样本外量化的能力。一种方法是识别哪些过去的训练信息现在已经过时，并删除它们，模型相应地更新其参数。需要对现有的性能指标有很好的了解，以了解可能仍然缺失的内容，并最终开发其他有用的指标，以更新模型并补偿异常和漂移数据。本研究的长期目标是使连续元学习模型适用于现实世界的数据，如时间序列数据或时空数据，以解决环境问题。研究方法的新颖性：神经过程是一个相对较新的架构，最初由Garnelo等人提出[2018b，a]。Jha等人[2022]对神经过程的最新应用进行了总结。据我们所知，神经过程在持续学习设置中的应用，或者在迁移学习中使用神经过程的集合，还没有被探索。此外，由于它们是最近才开发出来的，因此需要评估它们在实际环境中的适用性。随着新的动态和自适应模型的发展，我们很可能需要额外的指标来评估它们的特征和整体性能。这项研究与EPSRC的人工智能技术研究领域保持一致，因为这项研究是在一个机器学习小组与衍生的Mind Foundry合作进行的，该小组应用尖端的机器学习为商业和工业提供解决方案。

Context of the research and potential impactMeta learning, also referred to as "learning to learn", studies techniques and machine learning models able to adapt and generalise to new tasks and environments quickly, without having to go through a long training process every time. Continual learning focuses on the development of methods that can deal with a continuous stream of data.Advancements in continual meta-learning would hugely influence the application of modern machine learning models in industry, economy and society, where data is often received in a continuous stream. For machine learning models to be deployed in the real-world, they need to be able to detect model/data mismatch, concept drift, anomalies and out of sample quantification. The models require metrics to quantify when, how and to what extent performance is degrading, to determine when their parameters need to be updated.Our goal is to create models that can distinguish between meaningful and not meaningful changes in the data, and adapt to the first. Adaptive models need a dynamical architecture and they are appropriate when working with sequential streams of data. Detecting and quickly adapting to meaningful changes is critical in the real-world, for models' predictions to stay reliable.Aims and ObjectivesThe aim of this project is to advance the field of continual meta-learning, to enable a meaningful integration of machine learning models in industry settings and to deal with real-world data. The first task of the project is to provide an overlook of existing techniques. In particular, we will focus on Neural Processes and on extending them to the continual learning domain. Neural Processes are hybrid models, that like Gaussian Processes define distributions over functions, and have computationally efficient training like Neural Networks, with the ability to adapt their priors to data [Garnelo et al., 2018b,a]. Subsequent research can be done on the inclusion of meaningful inductive biases for and from multiple domains, and on the transfer learning abilities of ensembles/reservoirs of Neural Processes.During the DPhil we will explore the development of dynamical models with the ability of detecting model-data mismatch, concept drift, anomalies and out of sample quantification. An approach would be to recognise which past training information are now obsolete and remove them, with the model updating its parameters accordingly.A good knowledge of existing performance metrics is needed, to understand what may still be missing and eventually develop other metrics useful to update the model and compensate for anomalous and drifting data.A long-term goal of this research is to make continual meta-learning models applicable on real-world data, such as time series data or spatio temporal data to tackle environmental problems.Novelty of the research methodologyNeural Processes are a relatively new architecture, initially proposed by Garnelo et al. [2018b,a]. Jha et al. [2022] presents a summary of the most recent applications of Neural Processes. To our knowledge, the application of Neural Processes to continual learning settings, or using an ensemble of Neural Pro-cesses for transfer learning, have not been yet explored. Moreover, as their development is fairly recent, work needs to be done to assess their applicability in real-world settings.As new dynamical and adaptive models are developed, it is likely that we will need additional metrics to assess their features and overall performance.Alignment to EPSRC's strategies and research areasThe research aligns with the EPSRC research area of Artificial Intelligence technologies, as the research is carried out in a machine learning group in partnership with the spin-out Mind Foundry, that applies cutting edge machine learning to deliver solutions to business and industry.