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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Hierarchical Pooling for Sheaf Neural Networks
Jun 18, 2026Dionisia Naddeo, Carlo Abate, Pietro Liò +2Graph Neural Networks Graph Coarsening
UiT The Arctic University of Norway · UiT The Arctic University of Norway NORCE Norwegian Research Centre AS
Storyline Trees: Hierarchical Representations for Long-Form Narratives
Jun 18, 2026Litu Ou, Mirella LapataLong-Context Retrieval Long-Context QA
School of Informatics University of Edinburgh
Compact Geometric Representations of Hierarchies
Jun 16, 2026Prashant Gokhale, Piotr Indyk, Yuhao Liu +3Hierarchical Representation Learning Geometric Representation Learning
UW-Madison · MIT
Neural Network Implementation of the Renormalization Group for Fault Diagnosis with Class Imbalance
Jun 16, 2026Evgeny Nikulchev, Dmitry IlinClass-Imbalanced Learning Hierarchical Representation Learning
MIREA – Russian Technological University, Moscow, Russia
An expressivity analysis of hierarchical modelling in deep transformers via bounded-depth grammars
Jun 16, 2026Vinoth Nandakumar, Qiang Qu, Pramod Thebe +2Transformer Expressivity Hierarchical Representation Learning
University of Sydney · San Francisco State University · IIT Madras
MotionPyramid: Hierarchical Motion Representation and Residual Interfaces
Jun 15, 2026Gao Zhu, Zaishuo Xia, Yubei ChenLatent Action Learning Hierarchical Representation Learning
UC Davis
Hierarchical Fine-Grained Aerial Object Detection
Jun 15, 2026Yan Zhang, Fang Xu, Wen Yang +1Visual Representation Learning Hierarchical Representation Learning
School of Computer Science, Wuhan University, Wuhan, 430072, China · School of Artificial Intelligence, Wuhan University, Wuhan, 430072, China · School of Electronic Information, Wuhan University, Wuhan, 430072, China
SAE++: Cascaded Sparse Autoencoders Learn Multi-Level Visual Concepts in Multimodal LLMs
Jun 15, 2026Yusong Zhao, Hengyi Wang, Tanuja Ganu +2Hierarchical Representation Learning Multimodal Large Language Models
Rutgers University. · Microsoft.
Brownian Kernel Ladders
Jun 14, 2026Mahdi Mohammadigohari, Giuseppe Di Fatta, Giuseppe Nicosia +1Hierarchical Representation Learning Reproducing Kernel Hilbert Spaces
Faculty of Engineering, Free University of Bozen-Bolzano, Via Bruno Buozzi 1, Bolzano, 39100, Italy · Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, 95123, Santa Sofia, 97, Italy · Center for Applied Optimization, Department of Industrial and Systems Engineering, University of Florida, FL 32611, Gainesville, USA
Hierarchical Modeling of ICD Codes in EHR Foundation Models
Jun 13, 2026Megha Thukral, Dong Gyun Kang, Rudra Pratap Singh +3Graph Representation Learning Hierarchical Representation Learning
School of Interactive Computing, Georgia Institute of Technology, Atlanta, United States · Optum AI, United States
HoloRec: Holistic Encoding and Interleaved Reasoning for Generative Recommendation
Jun 13, 2026Shuqi Zhao, Jingsong Su, Xiang Liu +9Hierarchical Representation Learning Generative Recommendation
Institute of Information Engineering, Chinese Academy of Sciences Beijing, China · School of Artificial Intelligence, Beijing Normal University Beijing, China · JD.com,Beijing,China
Bridging Geographic Bias in Urban Streetscape Inference via Lifelong Learning with Visual-Semantic Pivoting
Jun 13, 2026Xinze ZhangGeospatial Representation Learning Continual Learning
University of Southern California, Los Angeles, CA 90007, USA
HiST: A Hierarchical Sparse Transformer for Cross-Modal Spatial Transcriptomics Modeling
Jun 12, 2026Weiyi Wu, Xinwen Xu, Xingjian Diao +4Computational Pathology Spatial Transcriptomics
Dartmouth College · Mass General Hospital · New Jersey Institute of Technology +1
Curvature-Guided Geometric Representation for Protein-Ligand Binding Affinity Prediction
Jun 12, 2026Shuai Li, Chuan-Xian Ren, Yuhao Li +4Protein-Ligand Binding Affinity Prediction Hierarchical Representation Learning
School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China · Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong
Tree-Structured Orthonormal Decomposition of the Aitchison Simplex
Jun 10, 2026Daisuke Yamada, Qijun Zhang, Travis Pence +3Hierarchical Representation Learning
University of Wisconsin Madison, Madison WI, USA.
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
Jun 6, 2026Alonso Urbano, David W. Romero, Max Zimmer +1Representation Learning Implicit Neural Representations
Department for AI in Society, Science, and Technology, Zuse Institute Berlin (ZIB), Germany · Cartesia AI, San Francisco, CA, USA · Institute of Mathematics, Technische Universität Berlin, Germany
Hierarchical Semantic-Constrained Heterogeneous Graph for Audio-Visual Event Localization
Jun 5, 2026Zhe Yang, Ruyi Zhang, Hongtao Chen +4Audio-Visual Understanding Heterogeneous GNNs
Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China · Peng Cheng Laboratory, Shenzhen 518000, China · Harbin Institute of Technology Suzhou Research Institute, Suzhou 215000, China
Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning
Jun 3, 2026Deepika SN Vemuri, Sayanta Adhikari, Ankit Saha +2Visual Representation Learning Interpretable ML
IIT Hyderabad · Amazon, India · KLA +1
Coarse-to-fine Hierarchical Architecture with Sequential Mamba for Brain Reconstruction
Jun 3, 2026Hoang-Son Vo, Van-Hung Bui, Minh-Huy Mai-Duc +2Hierarchical Representation Learning Visual SSMs
Chonnam National University, Gwangju, Republic of Korea · Vietnam National University - Ho Chi Minh City, University of Science, Vietnam · Institute for Cybersecurity and Digital Technologies, Russia
LDARNet: DNA Adaptive Representation Network with Learnable Tokenization for Genomic Modeling
Jun 3, 2026Daria Ledneva, Denis KuznetsovHierarchical Representation Learning Genomics
Moscow Independent Research Institute of Artificial Intelligence, Moscow, Russia.
SkelHCC: A Hyperbolic CLIP-Driven Cache Adaptation Framework for Skeleton-based One-Shot Action Recognition
Jun 2, 2026Yanan Liu, Anqi Zhu, Jingmin Zhu +6Skeleton-Based Action Recognition Cross-Modal Representation Learning
Yunnan University, Kunming, China · Monash University · Lancaster University +1
Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding
May 30, 2026Zaifei Yang, Samuel Ping-Man Choi, James KwokHierarchical Representation Learning Multimodal Embedding
Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong. · Lee Shau Kee School of Business and Administration, The Hong Kong Metropolitan University, Ho Man Tin, Kowloon, Hong Kong.
MIC: Maximizing Informational Capacity in Adaptive Representations via Isotropic Subspace Alignment
May 28, 2026Dang Nguyen Hong, Nhi Ngoc-Yen Nguyen, Huy-Hieu PhamRepresentation Learning Hierarchical Representation Learning
Hanoi University of Science and Technology, Hanoi, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam · College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam +1
Learn from your own latents and not from tokens: A sample-complexity theory
May 26, 2026Daniel J. Korchinski, Alessandro Favero, Matthieu WyartUnsupervised Learning Self-Supervised Learning
Department of Physics & Institute of Physics Johns Hopkins University & EPFL Baltimore, MD USA & Lausanne, VD Switzerland
Full-field prediction for engineering-scale three-dimensional aircraft with multigrid-hierarchical learning
May 26, 2026Yunfei Liu, Hao Wang, Yuhang Qi +8Hierarchical Representation Learning Computational Fluid Dynamics
Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, 621000,China. · Laboratory of Digitizing Software for Frontier Equipment,National University of Defense Technology,Changsha,410073,China. · National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Changsha,410073,China. +1
Machine Learning Multiscale Interactions
May 25, 2026Àlex Solé, Sergio Suárez-Dou, Albert Mosella-Montoro +4Machine Learning Interatomic Potentials Hierarchical Representation Learning
Image Processing Group – Signal Theory and Communications Department, Universitat Politècnica de Catalunya, Barcelona, Spain. · Inorganic and Organic Chemistry Department and Institute of Theoretical and Computational Chemistry, Universitat de Barcelona, Barcelona, Spain. · Department of Physics and Materials Science, University of Luxembourg, Luxembourg City, Luxembourg.
Learning Sparse Compositional Functions with Norm-Constrained Neural Networks
May 25, 2026Shuo Huang, Lorenzo Fiorito, Lorenzo Rosasco +1Neural Network Generalization Hierarchical Representation Learning
MaLGa, DIMA, Università degli Studi di Genova, Genoa, Italy · CBMM, Massachusetts Institute of Technology, Cambridge, MA, USA
HCL-FF: Hierarchical and Contrastive Learning for Forward-Forward Algorithm
May 24, 2026Jie-En Yao, Hong-En Chen, C. -C. Jay KuoHierarchical Representation Learning Forward-Forward Learning
University of Southern California
Hierarchical Concept Geometry in Language Models Emerges from Word Co-occurrence
May 22, 2026Andres Nava, Matthieu WyartNeural Representation Geometry Word Embeddings
Johns Hopkins University · EPFL & Johns Hopkins University
Uncovering the Latent Potential of Deep Intermediate Representations
May 21, 2026Arnesh Batra, Arush Gumber, Aniket Khandelwal +2Neural Representation Geometry Hierarchical Representation Learning
SBILab, Indraprastha Institute of Information Technology Delhi, Delhi, India.

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Hierarchical Pooling for Sheaf Neural Networks

Dionisia Naddeo, Carlo Abate, Pietro Liò, Nicola Toschi+1

UiT The Arctic University of Norway · UiT The Arctic University of Norway NORCE Norwegian Research Centre AS

Graph Neural Networks Graph Coarsening Graph Pooling Hierarchical Representation Learning Sheaf Neural Networks Sheaf Theory

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Sheaf Neural Networks (SNNs) generalize Graph Neural Networks (GNNs) by replacing scalar node signals with stalk-valued signals and by using restriction maps to measure compatibility across edges. Unlike standard graph diffusion, which encourages neighboring node features to become similar, sheaf diffusion promotes consistency through the restriction maps and can therefore model more general relationships between neighboring nodes. However, existing sheaf neural architectures mainly operate at a fixed graph resolution and do not provide a principled pooling mechanism for building hierarchical representations. In this paper, we introduce Hierarchical Sheaf Pool (HiSP), a sheaf-aware pooling framework based on local spectral coarsening. Given a partition of the graph, HiSP constructs each coarse stalk by projecting fine stalk-valued features onto the low-frequency eigenmodes of the cluster-internal sheaf Laplacian. These local modes define a cochain-level prolongation map, which allows the fine sheaf energy to be represented on the coarse space through a Galerkin operator. We further analyze the approximation induced by coarsening by separating truncation loss, due to discarded local modes, from realization loss, due to representing the projected operator as a coarse sheaf. Finally, we implement HiSP as a GNN pooling layer compatible with SNNs and provide a PyG implementation supporting batching, lifted sheaf Laplacians, and hierarchical architectures.

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