1 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
NysAct: A Scalable Preconditioned Gradient Descent using Nystrom Approximation
Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko
Adaptive gradient methods are computationally efficient and converge quickly, but they often suffer from poor generalization. In contrast, second-order methods enhance convergence…
cs.LG2025★ 1 cited
An Adaptive Method Stabilizing Activations for Enhanced Generalization
Hyunseok Seung, Jaewoo Lee, Hyunsuk Ko
We introduce AdaAct, a novel optimization algorithm that adjusts learning rates according to activation variance. Our method enhances the stability of neuron outputs by incorporati…
cs.LG2025
Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning
Qitao Tan, Jun Liu, Zheng Zhan +6
Large language models (LLMs) excel across various tasks, but standard first-order (FO) fine-tuning demands considerable memory, significantly limiting real-world deployment. Recent…