Binary classification is one of the most common problem in machine learning. It consists in predicting whether a given element belongs to a particular class. In this paper, a new algorithm for binary classification is proposed using a hypergraph representation. The method is agnostic to data representation, can work with multiple data sources or in non-metric spaces, and accommodates with missing values. As a result, it drastically reduces the need for data preprocessing or feature engineering. Each element to be classified is partitioned according to its interactions with the training set. For each class, a seminorm over the training set partition is learnt to represent the distribution of evidence supporting this class.
Empirical validation demonstrates its high potential on a wide range of well-known datasets and the results are compared to the state-of-the-art. The time complexity is given and empirically validated. Its robustness with regard to hyperparameter sensitivity is studied and compared to standard classification methods. Finally, the limitation of the model space is discussed, and some potential solutions proposed.
Reasoning about the relationships between entities from input facts (e.g., whether Ari is a grandparent of Charlie) generally requires explicit consideration of other entities that are not mentioned in the query (e.g., the parents of Charlie). In this paper, we present an approach for learning to solve problems of this kind in large, real-world domains, using sparse and local hypergraph neural networks (SpaLoc). SpaLoc is motivated by two observations from traditional logic-based reasoning: relational inferences usually apply locally (i.e., involve only a small number of individuals), and rela…
Seoul National University（首尔国立大学）；Hanyang University（汉阳大学）；AI Institute (AIIS), Seoul National University（首尔国立大学人工智能研究院（AIIS））
Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself. Answering complex questions that require multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the reasoning process and ii) high-order semantics of multi-hop knowledge facts need to be captured. In this paper, we introduce a concept of hypergraph to encode high-level semantics of a question and a knowledge base, and to learn high-order associations between them. The proposed mod…
Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning
organization= Center for Geospatial Sciences, Applications；Department of Landscape of Architecture；Urban Planning, Texas A\&M University , addressline= 788 Ross St , city= College Station , postcode= 77840 , state= TX , country= United States；organization= Zachry Department of Civil；Environmental Engineering, Texas A\&M University , addressline= 201 Dwight Look Engineering Building , city= College Station , postcode= 77843 , state= TX , country= United States；organization= Department of Civil；Environmental Engineering, University of Wisconsin-Madison , addressline= 1415 Engineering Dr , city= Madison , postcode= 53706 , state= WI , country= United States
Fraunhofer Institute for Industrial Engineering IAO（弗劳恩霍夫工业工程研究所）；University of Stuttgart, Institute of Human Factors and Technology Management IAT（斯图加特大学人类因素与技术管理研究所）；University of Leipzig（莱比锡大学）；Microsoft（微软）
Extracting information from unstructured text documents is a demanding task, since these documents can have a broad variety of different layouts and a non-trivial reading order, like it is the case for multi-column documents or nested tables. Additionally, many business documents are received in paper form, meaning that the textual contents need to be digitized before further analysis. Nonetheless, automatic detection and capturing of crucial document information like the sender address would boost many companies' processing efficiency. In this work we propose a hybrid approach that combines d…
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Laboratory（上海人工智能实验室）
Demystifying the interactions among multiple agents from their past trajectories is fundamental to precise and interpretable trajectory prediction. However, previous works only consider pair-wise interactions with limited relational reasoning. To promote more comprehensive interaction modeling for relational reasoning, we propose GroupNet, a multiscale hypergraph neural network, which is novel in terms of both interaction capturing and representation learning. From the aspect of interaction capturing, we propose a trainable multiscale hypergraph to capture both pair-wise and group-wise interac…
HGMEM: Hypergraph-based Working Memory to Improve Multi-step RAG for Long-Context Complex Relational Modeling
The Chinese University of Hong Kong.（香港中文大学）；Pengcheng Laboratory.（鹏城实验室）；WeChat AI, Tencent（微信AI，腾讯）；University of Chinese Academy of Sciences.（中国科学院大学）
Dementia-R1: Reinforced Pretraining and Reasoning from Unstructured Clinical Notes for Real-World Dementia Prognosis
Graduate School of AI, KAIST, Republic of Korea（韩国科学技术院人工智能研究生院）；Haeundae Paik Hospital, Inje University, Republic of Korea（韩国延世大学海云台医院）；Asan Medical Center, University of Ulsan College of Medicine, Republic of Korea（韩国釜山大学医学院阿桑医院）
CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction
Department of Computer Science University of Kentucky（计算机科学系 俄亥俄大学）；Department of Computer Science, Institute for Biomedical Informatics University of Kentucky（计算机科学系 生物医学信息学研究所 俄亥俄大学）
Discovering and Reasoning of Causality in the Hidden World with Large Language Models
TMLR Group, Hong Kong Baptist University（香港 Baptist 大学 TMLR 组）；Mohamed bin Zayed University of Artificial Intelligence（Mohamed bin Zayed 人工智能大学）；Department of Philosophy, Carnegie Mellon University（哲学系，卡内基梅隆大学）；School of Computer Science, The University of Sydney（悉尼大学计算机科学学院）；School of Mathematics and Statistics, The University of Melbourne（墨尔本大学数学与统计学学院）；The Chinese University of Hong Kong（香港中文大学）
Revealing Multimodal Causality with Large Language Models
University of Technology Sydney（技术大学悉尼大学）；Tongji University（同济大学）；University of Melbourne（墨尔本大学）；University of Sydney（悉尼大学）；Macquarie University（麦考瑞大学）
