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

• 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

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知觉任务方面取得了巨大的进步。例如，在医疗保健中，这些进步可以使这些进步可以使这些进步的应用铺平道路，而不是分类问题，这一点很重要：将机器学习系统视为产生类别预测，而且还产生了它们的原因。而且，这些推理的模式需要人类可以理解。为了实现这一目标，该项目将集中于人类与机器学习系统之间的知识交换，以及这种超出数据的知识交换如何导致更好的预测，而这些预测也是人类崩溃的。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工应用中的可用性。该项目的技术目标是沿两个广泛的主题开发的。第一个问题是一个问题：“我们如何在机器学习过程中涉及人类的反馈，以创建可解释并产生可解释的预测的简洁模型？”通过使人类作为关系知识的规则形式提供丰富的反馈，该项目旨在得出可解释的机器学习模型，这些模型与与人类的因果世界视图更兼容的简单解释相吻合。为了增强机器学习的可解释性，该项目将进一步探讨如何利用基于关系知识的人类反馈，以减少训练准确模型所需的数据集的大小。第二个问题是：“我们如何在推导可解释和可解释的推理模型时编码和利用关系信息？”该项目将探讨矢量空间和逻辑模型中关系知识的编码，并进一步研究如何将关系知识用于类似推理，语义理解和关系查询。该奖项反映了NSF的法定任务，并认为通过使用该基金会的知识分子和更广泛的影响来审查Criteria的评估。

项目成果

SIMPLE: A Gradient Estimator for k-subset sampling

简单：用于 k 子集采样的梯度估计器

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

Emergent analogical reasoning in large language models

• DOI: 10.1038/s41562-023-01659-w
• 发表时间: 2022-12
• 期刊: Nature Human Behaviour
• 影响因子: 29.9
• 作者: Taylor W. Webb;K. Holyoak;Hongjing Lu

Neuro-Symbolic Entropy Regularization

神经符号熵正则化

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

Pylon: A PyTorch Framework for Learning with Constraints

Pylon：用于约束学习的 PyTorch 框架

• DOI: 10.1609/aaai.v36i11.21711
• 发表时间: 2022
• 期刊: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track
• 作者: Ahmed, Kareem;Li, Tao;Ton, Thy;Guo, Quan;Chang, Kai-Wei;Kordjamshidi, Parisa;Srikumar, Vivek;Van den Broeck, Guy;Singh, Sameer
• 通讯作者: Singh, Sameer

A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints

具有逻辑约束的深度生成模型的伪语义损失

• 发表时间: 2023
• 期刊: Advances in Neural Information Processing Systems 36 (NeurIPS
• 作者: Ahmed, Kareem;Chang, Kai-Wei;Van den Broeck, Guy
• 通讯作者: Van den Broeck, Guy