30 citations · 44 across the 15 of their papers we have counts for
6 papers · 1 filter
Repulsor: Accelerating Generative Modeling with a Contrastive Memory Bank
Shaofeng Zhang, Xuanqi Chen, Ning Liao +7
The dominance of denoising generative models (e.g., diffusion, flow-matching) in visual synthesis is tempered by their substantial training costs and inefficiencies in representati…
Co-Training Vision Language Models for Remote Sensing Multi-task Learning
Qingyun Li, Shuran Ma, Junwei Luo +8
With Transformers achieving outstanding performance on individual remote sensing (RS) tasks, we are now approaching the realization of a unified model that excels across multiple t…
NTKMTL: Mitigating Task Imbalance in Multi-Task Learning from Neural Tangent Kernel Perspective
Xiaohan Qin, Xiaoxing Wang, Ning Liao +1
Multi-Task Learning (MTL) enables a single model to learn multiple tasks simultaneously, leveraging knowledge transfer among tasks for enhanced generalization, and has been widely…
ssToken: Self-modulated and Semantic-aware Token Selection for LLM Fine-tuning
Xiaohan Qin, Xiaoxing Wang, Ning Liao +5
Data quality plays a critical role in enhancing supervised fine-tuning (SFT) for large language models (LLMs), and token-level data selection has emerged as a promising direction f…
Innovator: Scientific Continued Pretraining with Fine-grained MoE Upcycling
Ning Liao, Xiaoxing Wang, Zehao Lin +18
A large language model (LLM) with knowledge in both scientific and general tasks is the foundation of science general intelligence. However, directly continued pretraining an LLM u…
Modeling All Response Surfaces in One for Conditional Search Spaces
Jiaxing Li, Wei Liu, Chao Xue +4
Bayesian Optimization (BO) is a sample-efficient black-box optimizer commonly used in search spaces where hyperparameters are independent. However, in many practical AutoML scenari…