The Intrinsic Dimension of Images and Its Impact on Learning

18 April 2021

Papers citing "The Intrinsic Dimension of Images and Its Impact on Learning"

47 / 47 papers shown

Title
Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights Zhaiming Shen Alex Havrilla Rongjie Lai A. Cloninger Wenjing Liao 39 0 0 06 May 2025
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling Theodoros Kouzelis Ioannis Kakogeorgiou Spyros Gidaris N. Komodakis DRL 75 5 0 17 Feb 2025
Beyond Fixed Horizons: A Theoretical Framework for Adaptive Denoising Diffusions Soren Christensen C. Strauch Lukas Trottner DiffM 95 0 0 31 Jan 2025
The Geometry of Tokens in Internal Representations of Large Language Models Karthik Viswanathan Yuri Gardinazzi Giada Panerai Alberto Cazzaniga Matteo Biagetti AIFin 88 4 0 17 Jan 2025
Adapting to Unknown Low-Dimensional Structures in Score-Based Diffusion Models Gen Li Yuling Yan DiffM 44 18 0 03 Jan 2025
Unsupervised detection of semantic correlations in big data Santiago Acevedo Alex Rodriguez A. Laio 65 2 0 04 Nov 2024
Offline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations Bryan Chan Anson Leung James Bergstra OffRL OnRL 52 0 0 19 Oct 2024
On the Relation Between Linear Diffusion and Power Iteration Dana Weitzner M. Delbracio P. Milanfar Raja Giryes DiffM 31 0 0 16 Oct 2024
Linear Convergence of Diffusion Models Under the Manifold Hypothesis Peter Potaptchik Iskander Azangulov George Deligiannidis DiffM 41 5 0 11 Oct 2024
Geometric Signatures of Compositionality Across a Language Model's Lifetime Jin Hwa Lee Thomas Jiralerspong Lei Yu Yoshua Bengio Emily Cheng CoGe 82 0 0 02 Oct 2024
Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds Hong Ye Tan Subhadip Mukherjee Junqi Tang Carola-Bibiane Schönlieb 52 0 0 13 Aug 2024
Many Perception Tasks are Highly Redundant Functions of their Input Data Rahul Ramesh Anthony Bisulco Ronald W. DiTullio Linran Wei Vijay Balasubramanian Kostas Daniilidis Pratik Chaudhari 38 2 0 18 Jul 2024
Reasoning in Large Language Models: A Geometric Perspective Romain Cosentino Sarath Shekkizhar LRM 44 2 0 02 Jul 2024
Generative Topological Networks Alona Levy-Jurgenson Z. Yakhini 38 0 0 21 Jun 2024
Just How Flexible are Neural Networks in Practice? Ravid Shwartz-Ziv Micah Goldblum Arpit Bansal C. B. Bruss Yann LeCun Andrew Gordon Wilson 37 4 0 17 Jun 2024
Emergence of a High-Dimensional Abstraction Phase in Language Transformers Emily Cheng Diego Doimo Corentin Kervadec Iuri Macocco Jade Yu A. Laio Marco Baroni 109 11 0 24 May 2024
Generative adversarial learning with optimal input dimension and its adaptive generator architecture Zhiyao Tan Ling Zhou Huazhen Lin GAN 28 0 0 06 May 2024
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data Saptarshi Chakraborty Peter L. Bartlett 36 1 0 24 Feb 2024
LDReg: Local Dimensionality Regularized Self-Supervised Learning Hanxun Huang R. Campello S. Erfani Xingjun Ma Michael E. Houle James Bailey 28 5 0 19 Jan 2024
Neural Network Approximation for Pessimistic Offline Reinforcement Learning Di Wu Yuling Jiao Li Shen Haizhao Yang Xiliang Lu OffRL 27 1 0 19 Dec 2023
Class Uncertainty: A Measure to Mitigate Class Imbalance Z. S. Baltaci K. Oksuz S. Kuzucu K. Tezoren B. K. Konar A. Ozkan Emre Akbas Sinan Kalkan 79 2 0 23 Nov 2023
Bridging Information-Theoretic and Geometric Compression in Language Models Emily Cheng Corentin Kervadec Marco Baroni 24 16 0 20 Oct 2023
AdaptNet: Policy Adaptation for Physics-Based Character Control Pei Xu Kaixiang Xie Sheldon Andrews P. Kry Michael Neff Morgan McGuire Ioannis Karamouzas Victor Zordan TTA 37 16 0 30 Sep 2023
Out-of-distribution detection using normalizing flows on the data manifold S. Razavi M. Mehmanchi Reshad Hosseini Mostafa Tavassolipour OODD 42 0 0 26 Aug 2023
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity V. Arora Daniele Irto Sebastian Goldt G. Sanguinetti 34 2 0 21 Jun 2023
Deep neural networks architectures from the perspective of manifold learning German Magai AAML AI4CE 16 6 0 06 Jun 2023
Mathematical Challenges in Deep Learning V. Nia Guojun Zhang I. Kobyzev Michael R. Metel Xinlin Li ... S. Hemati M. Asgharian Linglong Kong Wulong Liu Boxing Chen AI4CE VLM 35 1 0 24 Mar 2023
High Fidelity Image Synthesis With Deep VAEs In Latent Space Troy Luhman Eric Luhman DRL 3DV 26 7 0 23 Mar 2023
Deep Nonparametric Estimation of Intrinsic Data Structures by Chart Autoencoders: Generalization Error and Robustness Hao Liu Alex Havrilla Rongjie Lai Wenjing Liao 34 6 0 17 Mar 2023
The Effect of Data Dimensionality on Neural Network Prunability Zachary Ankner Alex Renda Gintare Karolina Dziugaite Jonathan Frankle Tian Jin 26 5 0 01 Dec 2022
Internal Representations of Vision Models Through the Lens of Frames on Data Manifolds Henry Kvinge Grayson Jorgenson Davis Brown Charles Godfrey Tegan H. Emerson 50 2 0 19 Nov 2022
Interpretable Dimensionality Reduction by Feature Preserving Manifold Approximation and Projection Yang Yang Hongjian Sun Jialei Gong Di Yu FAtt 26 2 0 17 Nov 2022
Instance-Dependent Generalization Bounds via Optimal Transport Songyan Hou Parnian Kassraie Anastasis Kratsios Andreas Krause Jonas Rothfuss 20 6 0 02 Nov 2022
A new method for determining Wasserstein 1 optimal transport maps from Kantorovich potentials, with deep learning applications Tristan Milne Étienne Bilocq A. Nachman OT 14 3 0 02 Nov 2022
Deep Double Descent via Smooth Interpolation Matteo Gamba Erik Englesson Marten Bjorkman Hossein Azizpour 53 10 0 21 Sep 2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry M. Penrod Harrison Termotto Varshini Reddy Jiayu Yao Finale Doshi-Velez Weiwei Pan AAML OOD 35 1 0 02 Aug 2022
Distance Learner: Incorporating Manifold Prior to Model Training Aditya Chetan Nipun Kwatra 21 1 0 14 Jul 2022
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning Damien Teney Maxime Peyrard Ehsan Abbasnejad 30 29 0 06 Jul 2022
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood Piotr Tempczyk Rafał Michaluk Łukasz Garncarek Przemysław Spurek Jacek Tabor Adam Goliñski 35 26 0 29 Jun 2022
Score-Based Generative Models Detect Manifolds Jakiw Pidstrigach DiffM 24 70 0 02 Jun 2022
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap Luca Pesce Bruno Loureiro Florent Krzakala Lenka Zdeborová 30 1 0 26 May 2022
Overparameterization Improves StyleGAN Inversion Yohan Poirier-Ginter Alexandre Lessard Ryan Smith Jean-François Lalonde 38 4 0 12 May 2022
Topology and geometry of data manifold in deep learning German Magai A. Ayzenberg AAML 19 11 0 19 Apr 2022
Towards Empirical Sandwich Bounds on the Rate-Distortion Function Yibo Yang Stephan Mandt 28 24 0 23 Nov 2021
Wasserstein GANs Work Because They Fail (to Approximate the Wasserstein Distance) Jan Stanczuk Christian Etmann L. Kreusser Carola-Bibiane Schönlieb GAN 16 48 0 02 Mar 2021
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018
Minimax Rates for Estimating the Dimension of a Manifold Jisu Kim Alessandro Rinaldo Larry A. Wasserman 160 24 0 03 May 2016