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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PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies
May 2, 2026Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son HyGraph Representation Learning Hierarchical Representation Learning
University of Alabama at Birmingham Birmingham, Alabama, USA
Learning Coarse-to-Fine Osteoarthritis Representations under Noisy Hierarchical Labels
May 1, 2026Tongxu ZhangHierarchical Classification Hierarchical Representation Learning
The Hong Kong Polytechnic University
SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models
May 1, 2026Shiqiang Cai, Nianhong Niu, Shizhu He +2Hierarchical Representation Learning
Institute of Automation, Chinese Academy of Sciences, Beijing, China · University of Science and Technology Beijing, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
Polaris: Coupled Orbital Polar Embeddings for Hierarchical Concept Learning
Apr 30, 2026Sahil Mishra, Srinitish Srinivasan, Sourish Dasgupta +1Hierarchical Classification Hierarchical Representation Learning
Indian Institute of Technology Delhi, New Delhi, India · KDM Lab, Dhirubhai Ambani University, Gandhinagar, Gujarat, India · Indian Institute of Technology Delhi, Abu Dhabi, UAE.
Action Motifs: Self-Supervised Hierarchical Representation of Human Body Movements
Apr 30, 2026Genki Kinoshita, Shu Nakamura, Ryo Kawahara +3Latent Action Learning Representation Learning
1Kyoto University · 2Kyoto Institute of Technology · 3RIKEN
TopoHR: Hierarchical Centerline Representation for Cyclic Topology Reasoning in Driving Scenes with Point-to-Instance Relations
Apr 27, 2026Yifeng Bai, Zhirong Chen, Bo Song +2Hierarchical Representation Learning Autonomous Driving Perception
Institute of Intelligent Machines, HFIPS, Chinese Academy of Sciences · University of Science and Technology of China · NullMax +1
h-MINT: Modeling Pocket-Ligand Binding with Hierarchical Molecular Interaction Network
Apr 25, 2026Yanru Qu, Yijie Zhang, Wenjuan Tan +6Protein-Ligand Binding Affinity Prediction Hierarchical Representation Learning
Department of Computer Science, UIUC · School of Computer Science, McGill University · MILA-Québec AI Institute +4
Multi-View Hierarchical Representation Learning of Fetal Hemodynamics for Maternal Hypertension Detection at the Edge
Apr 24, 2026Alireza Rafiei, Anahí Venzor Strader, Esteban Castro Aragón +6Hierarchical Representation Learning Clinical Prediction
Emory University, Atlanta, GA, USA · Center for Indigenous Health Research, Wuqu’ Kawoq | Maya Health Alliance, Tecpán, Guatemala · Emory University & Georgia Institute of Technology, Atlanta, GA, USA
HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering
Apr 22, 2026Yuyu Liu, Sarang Rajendra Patil, Mengjia Xu +1Healthcare Hierarchical Representation Learning
Department of Computer Science, Stony Brook University · Department of Data Science, New Jersey Institute of Technology · Department of Biomedical Informatics, Stony Brook University
Subgraph Concept Networks: Concept Levels in Graph Classification
Apr 20, 2026Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1Graph Neural Networks Hierarchical Representation Learning
University of Cambridge Cambridge, UK
Multi-View Hierarchical Graph Neural Network for Sketch-Based 3D Shape Retrieval
Apr 20, 2026Hang Cheng, Muyan He, Mingyu Fan +3Graph Neural Networks Hierarchical Representation Learning
HSG: Hyperbolic Scene Graph
Apr 19, 2026Liyang Wang, Zeyu Zhang, Hao TangGraph Representation Learning Hierarchical Representation Learning
School of Computer Science, Peking University
Multi-Granularity Reasoning for Natural Language Inference
Apr 18, 2026Chunling Xi, Di LiangHierarchical Representation Learning Natural Language Inference
1Pacific Insurance Technology Co., Ltd. · 2Lixin Information Services (Shenzhen) Co., Ltd.
Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering
Apr 18, 2026Tianyi Chen, Haobo Wang, Kai Tang +5Contrastive Learning Hierarchical Representation Learning
School of Software Technology, Zhejiang University, Zhejiang 310027, China · College of Computer Science and Technology, Zhejiang University, Zhejiang 310027, China · Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China +2
Hierarchical Vision Transformer Enhanced by Graph Convolutional Network for Image Classification
Apr 18, 2026Haibin JiaoVision Transformer Hierarchical Representation Learning
Independent Researcher
SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification
Apr 17, 2026Ke Xiong, Qian Wu, Wangjie Gan +2Hierarchical Classification Hierarchical Representation Learning
School of Software Technology, Zhejiang University, China · NetEase Yidun AI Lab, Hangzhou, China
BioHiCL: Hierarchical Multi-Label Contrastive Learning for Biomedical Retrieval with MeSH Labels
Apr 17, 2026Mengfei Lan, Lecheng Zheng, Halil KilicogluContrastive Learning Hierarchical Representation Learning
University of Illinois Urbana-Champaign
Uncertainty-guided Compositional Alignment with Part-to-Whole Semantic Representativeness in Hyperbolic Vision-Language Models
Mar 23, 2026Hayeon Kim, Ji Ha Jang, Junghun James Kim +1Vision-Language Models Hierarchical Representation Learning
Dept. of Electrical and Computer Engineering, Seoul National University, Republic of Korea · INMC & IPAI, Seoul National University, Republic of Korea
HiSAC: Hierarchical Sparse Activation Compression for Ultra-long Sequence Modeling in Recommenders
Feb 24, 2026Kun Yuan, Junyu Bi, Daixuan Cheng +5Sequential Recommendation Hierarchical Representation Learning
Alibaba Group · Beijing, China · Renmin University
WavePhaseNet: A DFT-Based Method for Constructing Semantic Conceptual Hierarchy Structures (SCHS)
Feb 16, 2026Kiyotaka Kasubuchi, Kazuo FukiyaHierarchical Representation Learning Dimensionality Reduction
[affiliation]
Orthogonal Hierarchical Decomposition for Structure-Aware Table Understanding with Large Language Models
Feb 2, 2026Bin Cao, Huixian Lu, Chenwen Ma +3Table QA Table Structure Recognition
Zhejiang University of Technology, China · Zhejiang Key Laboratory of Visual Information Intelligent Processing, China · University of Aberdeen, UK +1
Deep networks learn to parse uniform-depth context-free languages from local statistics
Jan 31, 2026Jack T. Parley, Francesco Cagnetta, Matthieu WyartHierarchical Representation Learning Statistical Learning Theory
Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · Theoretical and Scientific Data Science, SISSA, Trieste, Italy · Department of Physics and Astronomy, Johns Hopkins University, Baltimore, Maryland.
Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining
Jan 27, 2026Yunwei Ren, Yatin Dandi, Florent Krzakala +1Deep Learning Optimization Hierarchical Representation Learning
Princeton University · École Polytechnique Fédérale de Lausanne · University of California, Berkeley
The Urysohn Ladder: Recursive Metric Contraction for Scalable Continual Learning
Dec 20, 2025Xin LiContinual Learning Hierarchical Representation Learning
Department of Computer Science University at Albany Albany, NY 12222
CoFiRec: Coarse-to-Fine Tokenization for Generative Recommendation
Nov 27, 2025Tianxin Wei, Xuying Ning, Xuxing Chen +6Sequential Recommendation Hierarchical Representation Learning
University of Illinois Urbana-Champaign · Meta · University of California San Diego
Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders
Nov 7, 2025Mathias Rose Bjare, Giorgia Cantisani, Marco Pasini +2Audio Representation Learning Denoising Autoencoders
Johannes Kepler University, Linz, AT · STMS, CNRS, IRCAM, Sorbonne Universit´e, Paris, FR · Queen Mary University, London, UK +1
Talk in Pieces, See in Whole: Disentangled and Hierarchical Representation Learning in Language-based Object Detection
Sep 29, 2025Sojung An, Kwanyong Park, Yong Jae Lee +1Open-Vocabulary Object Detection Disentangled Representation Learning
Korea University · University of Seoul · University of Wisconsin-Madison
HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning
Sep 26, 2025Zhen-Hao Xie, Yan Wang, Lan Li +3Class-Incremental Learning Vision-Language Models
School of Artificial Intelligence and the State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation
Jun 17, 2025Lihuan Li, Hao Xue, Shuang Ao +2Intelligent Transportation Systems Hierarchical Representation Learning
University of New South Wales Sydney, Australia · Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Kronecker Factorization Improves Efficiency and Interpretability of Sparse Autoencoders
May 28, 2025Vadim Kurochkin, Yaroslav Aksenov, Daniil Laptev +2Representation Learning Activation Sparsity
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May 2, 2026, cs.LGJ/K move · Enter open · S save

PRIME: Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies

Viet Thanh Duy Nguyen, John K. Johnstone, Truong-Son Hy

University of Alabama at Birmingham Birmingham, Alabama, USA

Graph Representation Learning Hierarchical Representation Learning Equivariant GNNs Protein Representation Learning Molecular Representation Learning

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Proteins are inherently multiscale physical systems whose functional properties emerge from coordinated structural organization across multiple spatial resolutions, ranging from atomic interactions to global fold topology. However, existing protein representation learning methods typically operate at a single structural level or treat different sources of structural information as parallel modalities, without explicitly modeling their hierarchical relationships. We introduce PRIME (Protein Representation via Physics-Informed Multiscale Equivariant Hierarchies), a unified framework that models proteins as a nested family of five physically grounded structural graphs spanning surface, atomic, residue, secondary-structure, and protein levels. Adjacent levels are connected through deterministic, physics-informed assignment operators, enabling bidirectional information exchange via bottom-up aggregation and top-down contextual refinement. Experiments on standard protein representation learning benchmarks demonstrate strong and competitive performance across diverse tasks, with particularly notable gains on the Fold Classification benchmark, where PRIME outperforms the strongest geometric GNN baseline by margins of 13.80 and 18.30 points on the harder Superfamily and Fold splits, and achieves a state-of-the-art accuracy of 84.10% on Reaction Class prediction, surpassing all baseline methods, including ESM. Ablation studies confirm that each structural level contributes complementary and non-redundant information, and adaptive cross-attention analysis reveals that PRIME autonomously identifies the most task-relevant structural resolutions at prediction time. Our source code is publicly available at https://github.com/HySonLab/PRIME

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