Poster "principal component analysis" Papers

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A Geometric Analysis of PCA

Ayoub El Hanchi, Murat Erdogdu, Chris Maddison

NEURIPS 2025arXiv:2510.20978

Differentiable Optimization of Similarity Scores Between Models and Brains

Nathan Cloos, Moufan Li, Markus Siegel et al.

ICLR 2025arXiv:2407.07059

EigenGS Representation: From Eigenspace to Gaussian Image Space

LO-WEI TAI, Ching-En Ching En, Li et al.

CVPR 2025arXiv:2503.07446

PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection

Botao Ren, Xue Yang, Yi Yu et al.

ICLR 2025arXiv:2410.08210

Rethinking PCA Through Duality

Jan Quan, Johan Suykens, Panagiotis Patrinos

NEURIPS 2025arXiv:2510.18130

What Do Latent Action Models Actually Learn?

Chuheng Zhang, Tim Pearce, Pushi Zhang et al.

NEURIPS 2025arXiv:2506.15691

W-PCA Based Gradient-Free Proxy for Efficient Search of Lightweight Language Models

Shang Wang

ICLR 2025arXiv:2504.15983

On the Error-Propagation of Inexact Hotelling's Deflation for Principal Component Analysis

Fangshuo Liao, J. Lyle Kim, Cruz Barnum et al.

ICML 2024arXiv:2310.04283

Why do Variational Autoencoders Really Promote Disentanglement?

Pratik Bhowal, Achint Soni, Sirisha Rambhatla