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cs.LG2026★ 1 cited
FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics
Pingzhi Li, Hongxuan Li, Zirui Liu +2
Graph neural network (GNN) potentials such as SchNet improve the accuracy and transferability of molecular dynamics (MD) simulation by learning many-body interactions, but remain s…
cs.LG2025★ 1 cited
SC-LoRA: Balancing Efficient Fine-tuning and Knowledge Preservation via Subspace-Constrained LoRA
Minrui Luo, Fuhang Kuang, Yu Wang +2
Parameter-Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), are indispensable for efficiently customizing Large Language Models (LLMs). However, vanill…
cs.LG2025
Breaking the Frozen Subspace: Importance Sampling for Low-Rank Optimization in LLM Pretraining
Haochen Zhang, Junze Yin, Guanchu Wang +5
Low-rank optimization has emerged as a promising approach to enabling memory-efficient training of large language models (LLMs). Existing low-rank optimization methods typically pr…