1 citations · 1 across the 4 of their papers we have counts for
6 papers
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…
Brain-inspired and Self-based Artificial Intelligence
Yi Zeng, Feifei Zhao, Yuxuan Zhao +17
The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument…
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…
STREAM: Social data and knowledge collective intelligence platform for TRaining Ethical AI Models
Yuwei Wang, Enmeng Lu, Zizhe Ruan +2
This paper presents Social data and knowledge collective intelligence platform for TRaining Ethical AI Models (STREAM) to address the challenge of aligning AI models with human mor…