The scaling law, a strategy that involves the brute-force scaling of the
training dataset and learnable parameters, has become a prevalent approach for
developing stronger learning models. In this paper, we examine its rationale in
terms of learning from relational graphs. We demonstrate that directly adhering
to such a scaling law does not necessarily yield stronger models due to
architectural incompatibility and representation bottlenecks. To tackle this
challenge, we propose a novel framework for learning from relational graphs via
knowledge-aware parsimony learning. Our method draws inspiration from the
duality between data and knowledge inherent in these graphs. Specifically, we
first extract knowledge (like symbolic logic and physical laws) during the
learning process, and then apply combinatorial generalization to the task at
hand. This extracted knowledge serves as the ``building blocks'' for achieving
parsimony learning. By applying this philosophy to architecture, parameters,
and inference, we can effectively achieve versatile, sample-efficient, and
interpretable learning. Experimental results show that our proposed framework
surpasses methods that strictly follow the traditional scaling-up roadmap. This
highlights the importance of incorporating knowledge in the development of
next-generation learning technologies.

通过知识感知的简洁学习方法，从关系图中提取出知识并应用组合泛化，以有效实现样本高效、多功能和可解释的学习，超越传统的扩展路线图方法，强调在下一代学习技术的发展中融入知识的重要性。

知识感知的节俭学习：从关系图的视角

Knowledge-Aware Parsimony Learning: A Perspective from Relational Graphs

In this paper, we study a novel inference paradigm, termed as schema
inference, that learns to deductively infer the explainable predictions by
rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the
prevalent philosophical cognitive concept of schema. We strive to reformulate
the conventional model inference pipeline into a graph matching policy that
associates the extracted visual concepts of an image with the pre-computed
scene impression, by analogy with human reasoning mechanism via impression
matching. To this end, we devise an elaborated architecture, termed as
SchemaNet, as a dedicated instantiation of the proposed schema inference
concept, that models both the visual semantics of input instances and the
learned abstract imaginations of target categories as topological relational
graphs. Meanwhile, to capture and leverage the compositional contributions of
visual semantics in a global view, we also introduce a universal Feat2Graph
scheme in SchemaNet to establish the relational graphs that contain abundant
interaction information. Both the theoretical analysis and the experimental
results on several benchmarks demonstrate that the proposed schema inference
achieves encouraging performance and meanwhile yields a clear picture of the
deductive process leading to the predictions. Our code is available at
this https URL.

本文提出了一种基于 schema 推理的新型推理范式，该范式通过构建先前的深度神经网络（DNN）转发方案重建来学习演绎地推断出可解释的预测，同时建立了一种模型推断管道，利用人类推理机制的印象匹配类比，将图像中提取的视觉概念与先前计算得到的场景印象联系起来，建立了顶部关系图来模拟输入实例的视觉语义和目标类别的抽象想象。同时，引入了一种通用的 Feat2Graph 方案，可以在全局视角下捕捉和利用视觉语义的组成贡献，以建立包含丰富交互信息的关系图。该提出的 schema 推理方法在多个基准测试中达到了令人鼓舞的性能，并揭示了导致预测的演绎过程的清晰图片。

可解释图像分类模式推断

Schema Inference for Interpretable Image Classification

Modern deep transfer learning approaches have mainly focused on learning
generic feature vectors from one task that are transferable to other tasks,
such as word embeddings in language and pretrained convolutional features in
vision. However, these approaches usually transfer unary features and largely
ignore more structured graphical representations. This work explores the
possibility of learning generic latent relational graphs that capture
dependencies between pairs of data units (e.g., words or pixels) from
large-scale unlabeled data and transferring the graphs to downstream tasks. Our
proposed transfer learning framework improves performance on various tasks
including question answering, natural language inference, sentiment analysis,
and image classification. We also show that the learned graphs are generic
enough to be transferred to different embeddings on which the graphs have not
been trained (including GloVe embeddings, ELMo embeddings, and task-specific
RNN hidden unit), or embedding-free units such as image pixels.

本研究提出基于关系图的深度传递学习框架，从大规模未标记的数据中学习通用潜在关系图，并将其转移到各种下游任务中，包括问题回答、自然语言推理、情感分析和图像分类，表明所学习的关系图具有足够广泛的适用性。