Manifold Sampling

Manifold sampling focuses on efficiently and effectively drawing samples from complex, high-dimensional data that lie on or near a lower-dimensional manifold – a curved subspace within the higher-dimensional space. Current research emphasizes developing novel sampling methods, often incorporating techniques like Gaussian processes, variational gradient descent, and neural networks, to improve the accuracy and efficiency of sampling, particularly in constrained domains. These advancements have significant implications for various fields, including computer vision (e.g., improved 3D human motion capture and robust image augmentation), circuit design (e.g., more accurate yield prediction for SRAM components), and machine learning (e.g., enhanced Bayesian inference and optimization).

Papers

September 19, 2024

Manifold Sampling for Differentiable Uncertainty in Radiance Fields
Linjie Lyu, Ayush Tewari, Marc Habermann, Shunsuke Saito, Michael Zollhöfer, Thomas Leimkühler, Christian Theobalt
Radiance Field Illumination Model Manifold Sampling

July 28, 2023

Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold
Yanfang Liu, Guohao Dai, Wei W. Xing
Yield Prediction Manifold Sampling Manifold Optimization SRAM Cell Corresponding Yield Stress Value

November 20, 2022

Convolutional Filtering on Sampled Manifolds
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
Convolutional Filter Statistical Manifold Manifold Diffusion Manifold Sampling

October 20, 2022

Optimization on Manifolds via Graph Gaussian Processes
Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang
Optimization Purpose Gaussian Process Surrogate Model Regret Bound Class Manifold Manifold Sampling

October 12, 2022

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Ruqi Zhang, Qiang Liu, Xin T. Tong
Gradient Flow Open Sampling Domain Constraint Continuous Gradient Orthogonal Gradient Manifold Sampling

May 11, 2022

HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance
Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, Christian Theobalt
Motion Capture Contact Estimation Marker Less Mocap Plausible Pose Manifold Sampling

April 25, 2022

VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization
Minghui Chen, Cheng Wen, Feng Zheng, Fengxiang He, Ling Shao
Distribution Generalization Image Corruption Corruption Robustness Augmentation Approach Vicinal Transfer Augmentation Manifold Sampling

April 13, 2022

Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection
Haibao Li, Hongzhi Zhai
Multi Label Regularization Model Norm Regularization Manifold Regularization Manifold Sampling Multi Label Feature Selection Multicollinearity Issue

Manifold Sampling

Papers

Manifold Sampling for Differentiable Uncertainty in Radiance Fields

Seeking the Yield Barrier: High-Dimensional SRAM Evaluation Through Optimal Manifold

Convolutional Filtering on Sampled Manifolds

Optimization on Manifolds via Graph Gaussian Processes

Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent

HULC: 3D Human Motion Capture with Pose Manifold Sampling and Dense Contact Guidance

VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalization

Random Manifold Sampling and Joint Sparse Regularization for Multi-label Feature Selection