40 citations · 98 across the 12 of their papers we have counts for
12 papers
Unraveling the Real Working Mechanism and Inherent Flaws of GAE: A Method for Interpreting Transformer Processes from an Economic Perspective
Yongjin Cui, Xiaohui Fan
We observe a phenomenon that current algorithmic research in the field of explainable artificial intelligence primarily pursues better performance on several proxy metrics. On the…
Generic Interpretation Approach for Transformer Models Incorporating Heterogenous Attention Structures
Yongjin Cui, Xiaohui Fan, Huajun Chen
Transformer has significantly propelled the development of artificial intelligence, and certainly the development of agents as well. We categorize attention structures of Transform…
Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation
Yongjin Cui, Xiaohui Fan
Grad-ECLIP is published at ICML 2024 and represents a new Transformer interpretation technical route (intermediate features-based). First, this paper demonstrates that the intermed…
Transformer Interpretability from Perspective of Attention and Gradient
Yongjin Cui, Xiaohui Fan, Huajun Chen
Although researchers' attention is more focused on the performance of Transformer models, the interpretation of Transformer can never be ignored. Gradient is widely utilized in Tra…
The Neglected Baseline in Model Interpretation
Yongjin Cui, Xiaohui Fan
We observe that existing model interpretation methods generally ignore the baseline, and such neglect often results in imprecise or even incorrect interpretation. In this paper, we…
A Multi-Modal AI Copilot for Single-Cell Analysis with Instruction Following
Yin Fang, Xinle Deng, Kangwei Liu +5
Large language models excel at interpreting complex natural language instructions, enabling them to perform a wide range of tasks. In the life sciences, single-cell RNA sequencing…
ChatCell: Facilitating Single-Cell Analysis with Natural Language
Yin Fang, Kangwei Liu, Ningyu Zhang +7
As Large Language Models (LLMs) rapidly evolve, their influence in science is becoming increasingly prominent. The emerging capabilities of LLMs in task generalization and free-for…
Scientific Large Language Models: A Survey on Biological & Chemical Domains
Qiang Zhang, Keyang Ding, Tianwen Lyv +22
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelli…
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models
Yin Fang, Xiaozhuan Liang, Ningyu Zhang +5
Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However,…
Domain-Agnostic Molecular Generation with Chemical Feedback
Yin Fang, Ningyu Zhang, Zhuo Chen +3
The generation of molecules with desired properties has become increasingly popular, revolutionizing the way scientists design molecular structures and providing valuable support f…
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer
Yin Fang, Zhuo Chen, Xiaohui Fan +1
Machine learning, notably deep learning, has significantly propelled molecular investigations within the biochemical sphere. Traditionally, modeling for such research has centered…
Molecular Contrastive Learning with Chemical Element Knowledge Graph
Yin Fang, Qiang Zhang, Haihong Yang +7
Molecular representation learning contributes to multiple downstream tasks such as molecular property prediction and drug design. To properly represent molecules, graph contrastive…