"training dynamics" Papers
13 papers found
Beyond Random: Automatic Inner-loop Optimization in Dataset Distillation
Muquan Li, Hang Gou, Dongyang Zhang et al.
NeurIPS 2025posterarXiv:2510.04838
1
citations
Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks
Binghui Li, Zhixuan Pan, Kaifeng Lyu et al.
ICLR 2025posterarXiv:2410.10322
Flatness is Necessary, Neural Collapse is Not: Rethinking Generalization via Grokking
Ting Han, Linara Adilova, Henning Petzka et al.
NeurIPS 2025oralarXiv:2509.17738
3
citations
On the Feature Learning in Diffusion Models
Andi Han, Wei Huang, Yuan Cao et al.
ICLR 2025posterarXiv:2412.01021
13
citations
Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought
Jianhao Huang, Zixuan Wang, Jason Lee
ICLR 2025posterarXiv:2502.21212
18
citations
Dynamic Data Selection for Efficient SSL via Coarse-to-Fine Refinement
Aditay Tripathi, Pradeep Shenoy, Anirban Chakraborty
ECCV 2024poster
3
citations
Evolving Subnetwork Training for Large Language Models
hanqi li, Lu Chen, Da Ma et al.
ICML 2024poster
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
ICML 2024poster
Learning Associative Memories with Gradient Descent
Vivien Cabannnes, Berfin Simsek, Alberto Bietti
ICML 2024poster
Rethinking Fast Adversarial Training: A Splitting Technique To Overcome Catastrophic Overfitting
Masoumeh Zareapoor, Pourya Shamsolmoali
ECCV 2024poster
Stability-Informed Initialization of Neural Ordinary Differential Equations
Theodor Westny, Arman Mohammadi, Daniel Jung et al.
ICML 2024poster
United We Stand: Using Epoch-Wise Agreement of Ensembles to Combat Overfit
Uri Stern, Daniel Shwartz, Daphna Weinshall
AAAI 2024paperarXiv:2310.11077
What is Dataset Distillation Learning?
William Yang, Ye Zhu, Zhiwei Deng et al.
ICML 2024poster