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Collaborative Research: RI: AF: Medium: Exchanging Knowledge Beyond Data Between Human and Machine Learner

协作研究：RI：AF：媒介：在人类和机器学习者之间交换数据之外的知识

基本信息

批准号：
1956441
负责人：
Guy Van den Broeck
金额：
$ 49.89万
依托单位：
University of California-Los Angeles
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956441&HistoricalAwards=false
关键词：
Collaborative Research RI AF Medium

项目摘要

Recent advances in deep learning have made dramatic progress in solving basic perceptual tasks such as speech recognition and object detection. To pave the way for the many human-centered applications that these advances might enable, in healthcare for instance, it is important to move beyond classification problems: to think of machine learning systems as producing not just category predictions, but also the reasons for them. Moreover, these patterns of reasoning need to be comprehensible to humans. To enable this, this project will focus on the exchange of knowledge between humans and machine learning systems and how such exchange of knowledge beyond data can lead to better predictions that are also human-interpretable. The project will result in technological advances that will have the potential to significantly impact the usability of machine learning in human-facing applications.The technical aims of this project are developed along two broad themes. The first addresses the question, "How can we involve human feedback in the machine learning process to create succinct models that are interpretable and generate predictions that are explainable?" By enabling humans to provide rich feedback in the form of rules-of-thumb as relational knowledge, the project aims to derive succinct interpretable machine learning models that are amenable to simple explanations that are more compatible with the causal world-view of humans. To enhance the interpretability of machine learning, the project will further explore how human feedback based on relational knowledge can be leveraged to reduce the size of data sets required to train accurate models. The second addresses the question, "How can we encode and exploit relational information in deriving interpretable and explainable models for reasoning?" The project will explore the encoding of relational knowledge in both vector spaces and logical models and further investigate how relational knowledge can be used for analogical reasoning, semantic understanding, and relational queries.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

深度学习的最新进展在解决语音识别和对象检测等基本感知任务方面取得了巨大进展。为了为这些进步可能实现的许多以人为本的应用铺平道路，例如在医疗保健领域，重要的是超越分类问题：将机器学习系统视为不仅产生类别预测，而且还产生它们的原因。此外，这些推理模式需要被人类理解。为了实现这一目标，该项目将重点关注人类和机器学习系统之间的知识交换，以及这种超越数据的知识交换如何能够带来更好的预测，这些预测也是人类可以解释的。该项目将导致技术进步，这将有可能显着影响机器学习在面向人类的应用程序中的可用性。该项目的技术目标是沿着沿着两大主题发展。第一个解决了这个问题，“我们如何在机器学习过程中引入人类反馈，以创建可解释的简洁模型并生成可解释的预测？通过使人类能够以经验法则的形式提供丰富的反馈作为关系知识，该项目旨在获得简洁的可解释机器学习模型，这些模型可以进行简单的解释，更符合人类的因果世界观。为了增强机器学习的可解释性，该项目将进一步探索如何利用基于关系知识的人类反馈来减少训练准确模型所需的数据集的大小。第二个解决的问题，“我们如何编码和利用关系信息，推导出可解释和可解释的推理模型？“该项目将探索向量空间和逻辑模型中的关系知识编码，并进一步研究如何将关系知识用于类比推理、语义理解和关系查询。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（22）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

SIMPLE: A Gradient Estimator for k-subset sampling

简单：用于 k 子集采样的梯度估计器

DOI：
发表时间：
2023
期刊：
Proceedings of the International Conference on Learning Representations (ICLR
影响因子：
0
作者：
Ahmed, Kareem;Zeng, Zhe;Niepert, Mathias;Van den Broeck, Guy
通讯作者：
Van den Broeck, Guy

Neuro-Symbolic Entropy Regularization

神经符号熵正则化

DOI：
发表时间：
2022
期刊：
Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI
影响因子：
0
作者：
Ahmed, K.;Wang, E.;Chang, KW.;Van den Broeck, G.
通讯作者：
Van den Broeck, G.

Emergent analogical reasoning in large language models