7 citations · 20 across the 40 of their papers we have counts for
6 papers · 1 filter
KANLab: TensorFlow Playground for Kolmogorov-Arnold Networks
Lukas Niehaus, Robin Rawiel, Ulf Krumnack
KANLab is a browser-based visualization tool designed to interactively teach the core concepts of Kolmogorov-Arnold Networks (KAN), forked from the widely successful TensorFlow Pla…
CD-LoRA: Consistency-Driven Low-Rank Adaptation for Multi-Task Fine-Tuning
Qian Zha, Jinda Liu, Yuan Wu +1
While Multi-Task Learning (MTL) is essential for adapting Large Language Models (LLMs) to diverse domains, prevailing LoRA-based methods rely on complex routing mechanisms that par…
SOS-LoRA: Static Orthogonal-Subspace Low-Rank Adaptation with Fixed Multi-Scale Scaling
Yupeng Chang, Yuan Wu, Yi Chang
Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method for large language models. Under a fixed rank budget, LoRA parameterizes each adapted weig…
AGGC: Adaptive Group Gradient Clipping for Stabilizing Large Language Model Training
Zhiyuan Li, Yuan Wu, Yi Chang
To stabilize the training of Large Language Models (LLMs), gradient clipping is a nearly ubiquitous heuristic used to alleviate exploding gradients. However, traditional global nor…
R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-Task Learning
Jinda Liu, Yi Chang, Yuan Wu
Fine-tuning large language models (LLMs) is computationally expensive, and Low-Rank Adaptation (LoRA) provides a cost-effective solution by approximating weight updates through low…
A Survey on Data Augmentation in Large Model Era
Yue Zhou, Chenlu Guo, Xu Wang +2
Large models, encompassing large language and diffusion models, have shown exceptional promise in approximating human-level intelligence, garnering significant interest from both a…