arXiv Machine Learning
Discovering Data Manifold Geometry through Geometric Properties
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
Hugging Face Trending Papers
5d ago
Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models
Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model predictions, raising a basic question: what local metric bes...
arXiv AI
Aug 11
Three Necessary Principles for Self-Supervised Visual Representation Learning
arXiv:2608. 08309v1 Announce Type: cross Abstract: We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy.
arXiv Machine Learning
4d ago
Fisher-IRG: Fisher-Induced Local Invariant Representation Geometry across Language and Vision Models
arXiv:2609.36458v1 Announce Type: new Abstract: Semantic-preserving transformations can induce substantial motion in learned representations, while small changes may strongly affect model predictions...
arXiv AI
Jun 2
Global Geometry Is Not Enough for Vision Representations
arXiv:2602. 03282v2 Announce Type: replace-cross Abstract: A common assumption in representation learning is that globally well-distributed embeddings support robust and generalizable representations.
arXiv Machine Learning
Jun 15
Equivariant Representation Learning via Class-Pose Decomposition
arXiv:2207. 03116v4 Announce Type: replace Abstract: We introduce a general method for learning representations that are equivariant to symmetries of data.
arXiv Machine Learning
Sep 1
Partially Linear Autoencoders for Manifold Learning and Dimensionality Reduction
arXiv:2608.29867v1 Announce Type: new Abstract: Autoencoders are widely used for nonlinear dimensionality reduction and manifold learning. While most common implementations rely on both nonlinear enc...