1 citations · 1 across the 4 of their papers we have counts for
5 papers
Knowledge Graph Embeddings with Representing Relations as Annular Sectors
Huiling Zhu, Yingqi Zeng
Knowledge graphs (KGs), structured as multi-relational data of entities and relations, are vital for tasks like data analysis and recommendation systems. Knowledge graph completion…
TriAdaptLoRA: Brain-Inspired Triangular Adaptive Low-Rank Adaptation for Parameter-Efficient Fine-Tuning
Yao Liang, Yuwei Wang, Yi Zeng
The fine-tuning of Large Language Models (LLMs) is pivotal for achieving optimal performance across diverse downstream tasks. However, while full fine-tuning delivers superior resu…
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning
Yao Liang, Yuwei Wang, Yang Li +1
Fine-tuning techniques based on Large Pretrained Language Models (LPLMs) have been proven to significantly enhance model performance on a variety of downstream tasks and effectivel…
BrainKnow -- Extracting, Linking, and Synthesizing Neuroscience Knowledge
Cunqing Huangfu, Kang Sun, Yi Zeng +3
The exponential growth of neuroscience literature presents a significant challenge for researchers seeking to efficiently access and utilize relevant information. To address this i…
A Brain-inspired Computational Model for Human-like Concept Learning
Yuwei Wang, Yi Zeng
Concept learning is a fundamental aspect of human cognition and plays a critical role in mental processes such as categorization, reasoning, memory, and decision-making. Researcher…