5 citations · 7 across the 9 of their papers we have counts for
9 papers
Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages
Hongyang Chen, Zhongwu Sun, Hongfei Ye +2
Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating ca…
GeoGPT-RAG Technical Report
Fei Huang, Fan Wu, Zeqing Zhang +4
GeoGPT is an open large language model system built to advance research in the geosciences. To enhance its domain-specific capabilities, we integrated Retrieval Augmented Generatio…
SEDEG:Sequential Enhancement of Decoder and Encoder's Generality for Class Incremental Learning with Small Memory
Hongyang Chen, Shaoling Pu, Lingyu Zheng +1
In incremental learning, enhancing the generality of knowledge is crucial for adapting to dynamic data inputs. It can develop generalized representations or more balanced decision…
UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery
Ruifeng Li, Mingqian Li, Wei Liu +5
Drug discovery is crucial for identifying candidate drugs for various diseases.However, its low success rate often results in a scarcity of annotations, posing a few-shot learning…
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery
Ruifeng Li, Wei Liu, Xiangxin Zhou +4
In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular propert…
Dual-Label Learning With Irregularly Present Labels
Mingqian Li, Qiao Han, Ruifeng Li +2
In multi-task learning, labels are often missing irregularly across samples, which can be fully labeled, partially labeled or unlabeled. The irregular label presence often appears…
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs
Ruifeng Li, Mingqian Li, Wei Liu +1
Effective molecular representation learning is crucial for advancing molecular property prediction and drug design. Mainstream molecular representation learning approaches are base…
Physical formula enhanced multi-task learning for pharmacokinetics prediction
Ruifeng Li, Dongzhan Zhou, Ancheng Shen +9
Artificial intelligence (AI) technology has demonstrated remarkable potential in drug dis-covery, where pharmacokinetics plays a crucial role in determining the dosage, safety, and…
Learning Invariant Molecular Representation in Latent Discrete Space
Xiang Zhuang, Qiang Zhang, Keyan Ding +5
Molecular representation learning lays the foundation for drug discovery. However, existing methods suffer from poor out-of-distribution (OOD) generalization, particularly when dat…