arXiv (All)
open access
From Parallel to Sequential: Small-Batch Noise Restores Simplicity Bias of Muon
Simplicity bias, which can arise from sequential feature learning during training, has been proposed as a general explanation for the success of deep learning. An important driver of this phenomenon is saddle-to-saddle behavior, in which the gradient flow trajectory remains close to a sequence of sa
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Tight Complexity of Muon under Heavy-Tailed Noise#1326289
- Energy-Efficient Deep Learning Without Backpropagation: A Rigorous Hardware-Validated Benchmarking Study of Forward-Only Algorithms#1327284
- Eigenvalues of the Hessian in Deep Learning: The Origin of Symmetry and Its Breaking#1234286
- Gradient descent dynamics for deep equilibrium models#1198845
- Blockwise Stabilized Adaptive Cubic Regularization with Subsolvers via Recurrence#1356158
Record · ID 1341750
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive