Stabilizing the Dynamic Low-Rank Training

arXiv:2609.32615 · cs.LG · Submitted 2026-09-26 · Read on arXiv

cs.LG

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Accuracy — 20× in this paper · explained in 1 episode
• Pruning — 17× in this paper · explained in 4 episodes
• Gradient Flow — 13× in this paper · explained in 2 episodes
• Fine-Tuning — 6× in this paper · explained in 5 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 4× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Stabilized Dynamic Low-Rank Training — 2× in this paper
• Ambient Dimension — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Elias — 1× in this paper · explained in 2 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning (PEFT) — 1× in this paper · explained in 4 episodes
• Quantization — 1× in this paper · explained in 12 episodes
• Singular Value Decomposition — 1× in this paper
• Singular Value Decomposition (SVD) — 1× in this paper · explained in 2 episodes
• Spectral Gap — 1× in this paper · explained in 5 episodes
• Stiefel manifold — 1× in this paper · explained in 2 episodes
• Transferability — 1× in this paper · explained in 4 episodes

Sources

• Low-Rank Training of Deep Neural Networks for Emerging Memory Technology
• Stabilizing the Lottery Ticket Hypothesis
• Successfully Applying Lottery Ticket Hypothesis to Diffusion Model
• SGD and Weight Decay Secretly Minimize the Rank of Your Neural Network

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks