arXiv Machine Learning
Traversing the solution space of neural networks with Hessian Null Space Continuation
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Computer Vision
Sep 14
DRDN: Decoupled Representation Dynamic Network for From-Scratch ViT Class-Incremental Learning
arXiv:2607.01630v2 Announce Type: replace Abstract: Dynamic expansion methods for class-incremental learning (CIL) protect task-specific knowledge by growing dedicated tokens or subnetworks, yet our...
arXiv Machine Learning
Jul 8
Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity
arXiv:2607. 05546v1 Announce Type: cross Abstract: We develop a unified function space theory of deep fully connected neural networks.
arXiv Machine Learning
Jul 13
How are linear representations learned? Exact solutions to the dynamics of abstraction
arXiv:2607. 08843v1 Announce Type: new Abstract: In artificial and biological neural networks, concepts are often encoded as consistent linear directions in representation space.
Hugging Face Trending Papers
Jun 22
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain incomplete. From a statistical viewpoint, a natural question is: can DNNs attain feature-learning and prediction consistency comparable to that of classical models?
arXiv Machine Learning
Jun 17
A Dynamical Systems Perspective on the Analysis of Neural Networks
arXiv:2507. 05164v2 Announce Type: replace-cross Abstract: In this chapter, we utilize dynamical systems to analyze several aspects of machine learning algorithms.
arXiv Machine Learning
Aug 11
Input convex neural networks as surrogates in mathematical optimisation
arXiv:2608. 09707v1 Announce Type: cross Abstract: Embedding trained neural networks as surrogates within optimisation problems is an established practice in operations research.