Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning networks in pairs, the literature on multi-network alignment is sparse due to the exponentially growing solution space and lack of high-order discrepancy measures. To fill this gap, we propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. To handle the large solution space, multiple networks are decomposed into smaller aligned clusters via the fused Gromov-Wasserstein (FGW) barycenter. To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly. A fast proximal point method is further developed with guaranteed convergence to a local optimum. Extensive experiments and analysis show that our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.
Normalize-Then-Precondition: A Hierarchical Approach to Marginal Scale and Interaction Geometry for LLM Training
Gaoling School of Artificial Intelligence（高瓴人工智能学院）；Renmin University of China（中国人民大学）；School of Statistics and Management（统计与管理学院）；Shanghai University of Finance and Economics（上海财经大学）
Hierarchical Structure-Property Alignment for Data-Efficient Molecular Generation and Editing
School of Computer Science and Engineering（计算机科学与工程学院）；Central South University（中南大学）；Institute for Infocomm Research Agency for Science, Technology and Research (A* STAR)（信息通信研究所（A* STAR））
Beyond Trajectory Matching: Reflow with Marginal Distribution Alignment
Department of Statistics and Data Science, Tsinghua University（统计与数据科学系，清华大学）；Department of Computer Science and Technology, Tsinghua University（计算机科学与技术系，清华大学）；Bytedance Inc（字节跳动公司）
DashFusion: Dual-stream Alignment with Hierarchical Bottleneck Fusion for Multimodal Sentiment Analysis
School of Artificial Intelligence, Beijing University of Posts and Telecommunications（北京邮电大学人工智能学院）；Beijing National Research Center for Information Science and Technology, Tsinghua University（清华大学信息科学与技术国家研究中心）；Department of Automation, Tsinghua University（清华大学自动化系）
HIGHT: Hierarchical Graph Tokenization for Molecule-Language Alignment
MBZUAI（穆罕默德·本·扎耶德人工智能大学）；Carnegie Mellon University（卡内基梅隆大学）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；University of Maryland, College Park（马里兰大学学院市分校）；National University of Singapore（新加坡国立大学）
HiPath: Hierarchical Vision-Language Alignment for Structured Pathology Report Prediction
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机科学与电子工程学院）；Department of Bioengineering and Imperial-X, Imperial College London（帝国理工学院伦敦校区生物工程系）；Department of Pathology, Xiangtan Maternal and Child Health Hospital（湘潭 maternal and child health hospital pathology department）；Department of Pathology, The First People’s Hospital of Xiangtan City（湘潭市第一人民医院病理科）
ALaRM: Align Language Models via Hierarchical Rewards Modeling
Institute of Science and Technology for Brain-inspired Intelligence, Fudan University（复旦大学脑智能科学与技术研究院）；School of Data Science, Fudan University（复旦大学数据科学学院）；School of Computer Science, Fudan University（复旦大学计算机科学技术学院）；Research Institute of Intelligent and Complex Systems, Fudan University（复旦大学智能与复杂系统研究院）
Vision and Language Pretraining has become the prevalent approach for tackling multimodal downstream tasks. The current trend is to move towards ever larger models and pretraining datasets. This computational headlong rush does not seem reasonable in the long term to move toward sustainable solutions, and de facto excludes academic laboratories with limited resources. In this work, we propose a new framework, dubbed ViCHA, that efficiently exploits the input data to boost the learning by: (a) a new hierarchical cross-modal alignment loss, (b) new self-supervised scheme based on masked image mo…
MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment
Columbia University（哥伦比亚大学）；University of California, Berkeley（加州大学伯克利分校）；School of Information Systems and Management, Carnegie Mellon University（信息系统与管理学院，卡内基梅隆大学）；Department of Mathematics, University of Southern California（数学系，南加州大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）；Computer Science Department, UC San Diego（计算机科学系，UCSD）
SIGMMA: Hierarchical Graph-Based Multi-Scale Multi-modal Contrastive Alignment of Histopathology Image and Spatial Transcriptome
Wellcome Sanger Institute（沃森桑格研究所）；Cambridge Centre for AI in Medicine（剑桥人工智能医学中心）；Institute of AI for Health（人工智能与健康研究所）；Cambridge Stem Cell Institute（剑桥干细胞研究所）
Homogeneous Semantic Alignment and Hierarchical Expert Routing for Radiology Report Generation
Xinjiang University（新疆大学）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；Tsinghua University（清华大学）
