We study generalization and knowledge reuse capabilities of deep neural
networks in the domain of abstract visual reasoning (AVR), employing Raven's
Progressive Matrices (RPMs), a recognized benchmark task for assessing AVR
abilities. Two knowledge transfer scenarios referring to the I-RAVEN dataset
are investigated. Firstly, inspired by generalization assessment capabilities
of the PGM dataset and popularity of I-RAVEN, we introduce
Attributeless-I-RAVEN, a benchmark with four generalization regimes that allow
to test generalization of abstract rules applied to held-out attributes.
Secondly, we construct I-RAVEN-Mesh, a dataset that enriches RPMs with a novel
component structure comprising line-based patterns, facilitating assessment of
progressive knowledge acquisition in transfer learning setting. The developed
benchmarks reveal shortcomings of the contemporary deep learning models, which
we partly address with Pathways of Normalized Group Convolution (PoNG) model, a
novel neural architecture for solving AVR tasks. PoNG excels in both presented
challenges, as well as the standard I-RAVEN and PGM setups.

研究在抽象视觉推理领域中，深度神经网络的泛化和知识重用能力，通过使用 Raven's Progressive Matrices（RPMs）作为评估抽象视觉推理能力的基准任务。通过研究两种知识转移场景，介绍了 Attributeless-I-RAVEN 和 I-RAVEN-Mesh 两个数据集，以及一种名为 PoNG 的新型神经架构，用于解决抽象视觉推理任务。

抽象视觉推理模型中的泛化和知识迁移

Generalization and Knowledge Transfer in Abstract Visual Reasoning  Models

As the cost of training ever larger language models has grown, so has the
interest in reusing previously learnt knowledge. Transfer learning methods have
shown how reusing non-task-specific knowledge can help in subsequent
task-specific learning. In this paper, we investigate the inverse: porting
whole functional modules that encode task-specific knowledge from one model to
another. We designed a study comprising 1,440 training/testing runs to test the
portability of modules trained by parameter-efficient finetuning (PEFT)
techniques, using sentiment analysis as an example task. We test portability in
a wide range of scenarios, involving different PEFT techniques and different
pretrained host models, among other dimensions. We compare the performance of
ported modules with that of equivalent modules trained (i) from scratch, and
(ii) from parameters sampled from the same distribution as the ported module.
We find that the ported modules far outperform the two alternatives tested, but
that there are interesting performance differences between the four PEFT
techniques. We conclude that task-specific knowledge in the form of
structurally modular sets of parameters as produced by PEFT techniques is
highly portable, but that degree of success depends on type of PEFT and on
differences between originating and receiving pretrained models.

通过使用参数高效微调技术训练的模块，研究探讨了任务特定知识在不同模型之间的可移植性，发现该方法在情感分析等任务上远远优于从头开始训练或从相同分布中采样参数训练的模块。

评估参数高效微调方法训练的参数矩阵的可移植性

Assessing the Portability of Parameter Matrices Trained by  Parameter-Efficient Finetuning Methods

When building a new application we are increasingly confronted with the need
of reusing and integrating pre-existing knowledge. Nevertheless, it is a fact
that this prior knowledge is virtually impossible to reuse as-is. This is true
also in domains, e.g., eHealth, where a lot of effort has been put into
developing high-quality standards and reference ontologies, e.g. FHIR1. In this
paper, we propose an integrated methodology, called iTelos, which enables data
and knowledge reuse towards the construction of Interoperable Electronic Health
Records (iEHR). The key intuition is that the data level and the schema level
of an application should be developed independently, thus allowing for maximum
flexibility in the reuse of the prior knowledge, but under the overall guidance
of the needs to be satisfied, formalized as competence queries. This intuition
is implemented by codifying all the requirements, including those concerning
reuse, as part of a purpose defined a priori, which is then used to drive a
middle-out development process where the application schema and data are
continuously aligned. The proposed methodology is validated through its
application to a large-scale case study.

本文介绍了一种名为 iTelos 的综合方法，可以使数据和知识重用以构建可互操作的电子健康记录（iEHR），并通过大规模案例研究验证了其有效性。

构建用途驱动的互操作电子健康记录知识图谱

Building Interoperable Electronic Health Records as Purpose-Driven  Knowledge Graphs

Deep reinforcement learning has shown an ability to achieve super-human
performance in solving complex reinforcement learning (RL) tasks only from
raw-pixels. However, it fails to reuse knowledge from previously learnt tasks
to solve new, unseen ones. Generalizing and reusing knowledge are the
fundamental requirements for creating a truly intelligent agent. This work
proposes a general method for one-to-one transfer learning based on generative
adversarial network model tailored to RL task.

该研究提出了一种基于生成对抗网络模型的一对一转移学习方法，旨在解决深度强化学习中新任务的知识重用和泛化问题。