The traditional Machine Learning (ML) methodology requires to fragment the
development and experimental process into disconnected iterations whose
feedback is used to guide design or tuning choices. This methodology has
multiple efficiency and scalability disadvantages, such as leading to spend
significant resources into the creation of multiple trial models that do not
contribute to the final solution.The presented work is based on the intuition
that defining ML models as modular and extensible artefacts allows to introduce
a novel ML development methodology enabling the integration of multiple design
and evaluation iterations into the continuous enrichment of a single unbounded
intelligent system. We define a novel method for the generation of dynamic
multitask ML models as a sequence of extensions and generalizations. We first
analyze the capabilities of the proposed method by using the standard ML
empirical evaluation methodology. Finally, we propose a novel continuous
development methodology that allows to dynamically extend a pre-existing
multitask large-scale ML system while analyzing the properties of the proposed
method extensions. This results in the generation of an ML model capable of
jointly solving 124 image classification tasks achieving state of the art
quality with improved size and compute cost.

研究利用模块化模型和连续开发方法来生成多任务机器学习模型，以实现多次设计和评估迭代的整合，从而生成一个能够解决 124 个图像分类任务的机器学习模型，并达到了最优质量和改进的规模和计算成本。

用于大规模多任务动态 ML 系统的持续发展方法论

A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems

We introduce an algorithm for model-based hierarchical reinforcement learning
to acquire self-contained transition and reward models suitable for
probabilistic planning at multiple levels of abstraction. We call this
framework Planning with Abstract Learned Models (PALM). By representing
subtasks symbolically using a new formal structure, the lifted abstract Markov
decision process (L-AMDP), PALM learns models that are independent and modular.
Through our experiments, we show how PALM integrates planning and execution,
facilitating a rapid and efficient learning of abstract, hierarchical models.
We also demonstrate the increased potential for learned models to be
transferred to new and related tasks.

该研究利用一种新的形式结构，提出了一种基于模型的层次强化学习算法，名为 PALM，可学习独立、模块化的转移和奖励模型用于概率规划，并演示了其将规划和执行进行集成，以快速有效地学习抽象、分层模型以及转移至新的相关任务的增强潜力。

抽象学习模型规划与可迁移子任务学习

Planning with Abstract Learned Models While Learning Transferable  Subtasks

Numerous models for grounded language understanding have been recently
proposed, including (i) generic models that can be easily adapted to any given
task and (ii) intuitively appealing modular models that require background
knowledge to be instantiated. We compare both types of models in how much they
lend themselves to a particular form of systematic generalization. Using a
synthetic VQA test, we evaluate which models are capable of reasoning about all
possible object pairs after training on only a small subset of them. Our
findings show that the generalization of modular models is much more systematic
and that it is highly sensitive to the module layout, i.e. to how exactly the
modules are connected. We furthermore investigate if modular models that
generalize well could be made more end-to-end by learning their layout and
parametrization. We find that end-to-end methods from prior work often learn
inappropriate layouts or parametrizations that do not facilitate systematic
generalization. Our results suggest that, in addition to modularity, systematic
generalization in language understanding may require explicit regularizers or
priors.

比较了通用模型和模块化模型在系统化泛化方面的不同，发现模块化模型更具系统性，且高度敏感于模块排列方式，还探究了一些方法，结果表明，除了模块化，语言理解的系统泛化可能需要显式的正则化器或先验。