Intrinsic Dimensionality

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4 papers in the last four weeks, down 20% on the four weeks before. 0.0% of all new papers.

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  1. Rethinking Intrinsic Dimension Estimation in Neural Representations

    Apr 22, 2026Rickmer Schulte, David RügamerIntrinsic DimensionalityNeural Representation Geometry

    LMU Munich, MCML

  2. Generalization at the Edge of Stability

    Apr 21, 2026Mario Tuci, Caner Korkmaz, Umut Şimşekli +1Intrinsic DimensionalityNeural Network Generalization

    INRIA, CNRS, Département d’Informatique de l’Ecole Normale Supérieure / PSL, France · Department of Computing, Imperial College London, United Kingdom

  3. Asymptotic Learning Curves for Diffusion Models with Random Features Score and Manifold Data

    Mar 24, 2026Anand Jerry George, Nicolas MacrisIntrinsic DimensionalityScore-Based Generative Modeling

    École Polytechnique Fédérale de Lausanne (EPFL), Lab for Statistical Mechanics of Inference in Large Systems (SMILS), CH-1015 Lausanne, Switzerland

  4. Geometry as a Missing Axis of Representation Quality: The Variational Geometric Information Bottleneck under Data Scarcity

    Nov 4, 2025Ronald KatendeIntrinsic DimensionalityInformation Bottleneck

    Department of Mathematics, Kabale University, Katuna Road, Kikungiri Hill, Kabale, 317, Uganda

  5. HomID : Benchmarking Intrinsic Dimension Estimators on Homogenous Manifolds with Anisotropic Embeddings

    Oct 1, 2025Aritra Das, Joseph T. Iosue, Victor V. AlbertIntrinsic DimensionalityManifold Learning

    Joint Center for Quantum Information and Computer Science (QuICS), NIST & UMD College Park. · Joint Quantum Institute (JQI), NIST & UMD College Park.

  6. Surprisingly High Redundancy in Electronic Structure Data Across Materials Explained by Low Intrinsic Dimensionality

    Jul 11, 2025Sazzad Hossain, Ponkrshnan Thiagarajan, Shashank Pathrudkar +4Intrinsic DimensionalityDataset Pruning

    Department of Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI, USA. · Hopkins Extreme Materials Institute, Johns Hopkins University, Baltimore, MD, USA. · Department of Materials Science and Engineering, University of California, Los Angeles, CA, USA. +1

  7. Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions

    Sep 4, 2024Peng Wang, Huijie Zhang, Zekai Zhang +3Intrinsic DimensionalitySample Complexity

    University of Macau · University of Michigan · University of California, Berkeley