Hierarchical Representation Learning

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18 papers in the last four weeks, up 260% on the four weeks before. 0.2% of all new papers.

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Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images
Aug 1, 2026Zhiwen Xu, Xiaoming Yan, Chengkun Wu +3Computational Pathology Spatial Transcriptomics
National University of Defense Technology Changsha, China
Multi-Granularity Position Embedding of Graphs via Granular-Ball for Link Prediction
Jul 31, 2026Sen Zhao, Cheng Liu, Shuyin Xia +4Graph Representation Learning Link Prediction
Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, Key Laboratory of Cyberspace Big Data Intelligent Security, Ministry of Education, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China · Chongqing Key Laboratory of Computational Intelligence, Key Laboratory of Big Data Intelligent Computing, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China · Chongqing Ant ConsumerFinance Co,. Ltd, Ant Group, Chongqing, 400065, China
TreeAdapter: Hierarchical Taxonomy-Guided Adapter Composition for Fine-Grained Species Image Generation
Jul 27, 2026Yuze Sun, Zhongjie Duan, Yingda ChenHierarchical Representation Learning Image Generation
ModelScope Team, Alibaba Group
A Statistical Difference between Single-Layer Learning and Hierarchical Learning in Wide Neural Networks
Jul 26, 2026Sumio WatanabeNeural Network Generalization Hierarchical Representation Learning
RIKEN Center for Advanced Intelligence Project 1-4-1 Nihonbashi, Chuo-ku, 103-0027, Tokyo, Japan
Gradient-Based Latent Decomposition Reveals Mechanisms of Feature Degradation in Weakly Supervised Mammography
Jul 24, 2026Vinceline Bertrand, Ionut CardeiVariational Autoencoders Hierarchical Representation Learning
Florida Atlantic University, Boca Raton, FL, USA
MSBraM: A Multi-scale Self-supervised Brain Foundation Model for Hierarchical EEG Dynamics Learning
Jul 23, 2026Tao Zhou, Jing Han, Lingyu Shu +1Electroencephalography Self-Supervised Pre-Training
Hunan University Changsha, China
Hierarchical Wireless Foundation Model for Multi-Task Optimization
Jul 18, 2026Yangjing Wang, Ouya Wang, Shenglong Zhou +1Wireless Foundation Models Hierarchical Representation Learning
Department of Electrical and Electronic Engineering, Faculty of Engineering, Imperial College London, SW7 2BX London, U.K. · School of Mathematics and Statistics, Beijing Jiaotong University, China
MIDI-RAE-JEPA: Hierarchical Representation Learning and Generation for Symbolic Music
Jul 16, 2026Scott H. HawleyRepresentation Learning Self-Supervised Pre-Training
Department of Chemistry & Physics, Belmont University, Nashville, TN, USA
Mitigating The Effect of Class Imbalance in Data with Hierarchical and Dependable Structure
Jul 13, 2026Bipin Chhetri, Deepika Giri, Avishek Kadel +2Class-Imbalanced Learning Hierarchical Classification
Department of Computer Science1, Texas Tech University1 · Cumberland University2 · Yeshiva University3 +1
HiHR: Hierarchical Hyperbolic Representation for Aerial-Ground Person Re-Identification
Jul 10, 2026Qiwei Yang, Pingping ZhangHierarchical Representation Learning Hyperbolic Representation Learning
Dalian University of Technology, Dalian, China
HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
Jul 9, 2026Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4Panoptic Segmentation Hierarchical Representation Learning
1Aalborg University, Denmark · 2Pioneer Centre for AI, Denmark · 3Aalto University, Finland +2
Navigating Hierarchy: Hyperbolic Learning on Brain Graphs for Disorder Diagnosis
Jul 8, 2026Yapeng Li, Bo Jiang, Ziyan Zhang +2Medical Diagnosis Hierarchical Representation Learning
School of Computer Science and Technology, Anhui University, 111 Jiulong Road, Hefei, 230601, China
HPR-SAM: Hierarchical Probabilistic Representation Learning for Prompt-free SAM-based Medical Image Segmentation
Jul 8, 2026Yingzhen Hu, Yiheng Zhong, Keying Zhu +5Medical Imaging Foundation Models Representation Learning
Yale University, United States
RFHNet: Relational and Frequency-Aware Hashing Network for Large-Scale Fine-Grained Food Image Retrieval
Jul 7, 2026Junsong Wang, Weiqing Min, Guorui Sheng +3Image Matching Fine-Grained Image Retrieval
College of Computer Science and Artificial Intelligence, Ludong University Yantai, China · Institute of Computing Technology, Chinese Academy of Sciences Beijing, China · University of Chinese Academy of Sciences Beijing, China
Is the Geometry Doing the Work? An Operating-Point Audit of Hierarchy in Hyperbolic Vision-Language Models
Jul 6, 2026Jaeyoung Kim, Eunseok Kim, Dongsuk JangVLM Evaluation Vision-Language Models
MADI
Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity
Jul 6, 2026Juhyoung Park, Jaehyuk Bae, Hyeonbo Yang +1Hierarchical Representation Learning Data-Efficient Learning
School of Computing 2Department of Brain and Cognitive Sciences Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea · Department of Brain and Cognitive Sciences Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
Jul 5, 2026Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula +10Contrastive Learning Hierarchical Representation Learning
TUM.ai, Munich, Germany · Ludwig-Maximilian-University, Munich, Germany · Technical University of Munich, Munich, Germany +6
Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics
Jul 4, 2026Zhoujie Hou, Song Wang, Kexin Lou +3Sleep Stage Classification Latent Dynamics Modeling
Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China · Omni-Intelligence, Shenzhen, China · Shenzhen Loop Area Institute, Shenzhen, China
LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection
Jul 3, 2026Hang Gao, Xiaoyu Chen, Baoquan Cui +4Heterogeneous GNNs Hierarchical Representation Learning
Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences
The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning
Jul 3, 2026Joan BrunaSingle-Index Models Hierarchical Representation Learning
Courant Institute School of Mathematics, Computing and Data Science, New York University
Learning Taxonomic Trees with Hierarchical Representation Regularization for Large Multimodal Models
Jul 3, 2026Hulingxiao He, Zhi Tan, Yuxin PengHierarchical Representation Learning Multimodal Large Language Models
Wangxuan Institute of Computer Technology, Peking University
HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures
Jul 2, 2026Ziyun Qiao, Yue Min, Ruining Chen +1Language Model Pretraining Training Data Curation
Wizard Quant, Beijing, China · University of Science and Technology of China, Hefei, China
SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition
Jul 2, 2026Yang Li, Pan Hu, Yan Zhang +3Graph Structure Learning Electroencephalography
School of Software Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
Hierarchical Self-Supervised Representation Learning Framework for Multivariate Time Series Grounded in ECG Analysis
Jul 1, 2026Siwon KimElectrocardiography Self-Supervised Time Series Representation Learning
Research Institute of Basic Sciences, Seoul National University, Seoul, Korea
HyperVLP: Enhancing Hierarchical Surgical Video-Language Pre-training in Hyperbolic Space
Jun 30, 2026Yaojun Hu, Kun Yuan, Nassir Navab +3Hierarchical Representation Learning Surgical Video Understanding
Zhejiang University, Hangzhou, China · University of Strasbourg, CNRS, INSERM, ICube, UMR7357, Strasbourg, France · IHU Strasbourg, Strasbourg, France +1
ScaleAware-JEPA: Latent Representation for Discovery in Multiscale Physical Fields
Jun 29, 2026Guang-Xing LiRepresentation Learning Joint-Embedding Predictive Architecture
MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment
Jun 27, 2026Xinjin Li, Mengyue Wang, Yuzhen Lin +4Intelligent Tutoring Systems Hierarchical Representation Learning
Columbia University, New York, USA · University of California, Berkeley, Berkeley, USA · School of Information Systems and Management, Carnegie Mellon University, Pittsburgh, PA, USA +4
Prism Transformer: Progressive Head Schedules for Hierarchical Attention Processing
Jun 25, 2026Shubham AggarwalHierarchical Representation Learning Transformer Attention
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Jun 22, 2026Sehwan Kim, Yan Sun, Faming LiangNeural Network Generalization Hierarchical Representation Learning
Ewha Womans University, Seoul 03760, Republic of Korea · New Jersey Institute of Technology, Newark, NJ 07102, USA · Purdue University, West Lafayette, IN 47907, USA
Do Sparse Autoencoders Learn Meaningful Concept Hierarchies?
Jun 22, 2026Nils Grandien, David Steinmann, Felix Friedrich +1Hierarchical Representation Learning Sparse Autoencoders
Computer Science Department, TU Darmstadt · Hessian Center for AI (hessian.AI) · Black Forest Labs +1

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Gene Ontology-Guided Hierarchical Spatial Gene Expression Prediction from Histopathology Images

Zhiwen Xu, Xiaoming Yan, Chengkun Wu, Juan Chen+2

National University of Defense Technology Changsha, China

Computational Pathology Spatial Transcriptomics Hierarchical Representation Learning Spatial Gene Expression Prediction Genomics Gene Expression Prediction from Histopathology Images

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Predicting spatial gene expression from histopathology images enables large-scale transcriptomic profiling without the cost of direct measurement. Existing methods decode the target gene set as a flat, unstructured vector, ignoring the inter-gene dependencies arising from shared biological pathways and regulatory programs. Without explicit structural guidance, models must infer these dependencies entirely from limited paired data, constraining prediction quality. We propose MSGR (Multi-Scale Gene Refiner), which bridges this gap by incorporating the Gene Ontology (GO), a curated functional hierarchy of genes, as an explicit structural prior. MSGR organizes target genes into a four-level GO tree. Its GO-guided decoder then progressively refines predictions from coarse functional domains to fine individual genes via residual corrections under scale-weighted supervision. Operating solely on the gene side, the GO-guided decoder serves as a seamless plug-in replacement that consistently improves existing architectures without requiring any image-side modifications. Extensive experiments on nine datasets from the HEST-1k benchmark provide empirical evidence for two central claims: GO-structured decoding consistently outperforms flat decoding, even against a state-of-the-art generative baseline, and the gain is attributable to biological ontology structure rather than hierarchical decomposition per se, as confirmed by a +0.027 margin over a structurally equivalent random hierarchy.

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Latest papers 154