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Yafei Liu

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AAAI Conference 2026 Short Paper

Atom-level Adaptive Receptive Fields: A Pruning-Based Encoder for 2D Molecular Graphs (Student Abstract)

  • Yuhao Zhang
  • Ningkang Peng
  • Yafei Liu
  • Lin Li
  • Masaru Kitsuregawa
  • Yanhui Gu

The two-dimensional (2D) graph structure of a molecule encodes abundant latent property information. A well-designed molecular graph encoder can capture informative low-dimensional dense representations of molecules, which can subsequently be applied to a widerange of downstream tasks. To achieve fine-grained anddiscriminative molecular representations that capture localized structural information, we propose an novel atom-level adaptive receptive field encoder, enabling each atomic node in the molecular graph to dynamically adjust its receptive field size. To the best of our knowledge, we are the first to introduce an effective rank-guided pruning strategy for 2D molecular graphs.

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IJCAI Conference 2022 Conference Paper

“My nose is running. ” “Are you also coughing? ”: Building A Medical Diagnosis Agent with Interpretable Inquiry Logics

  • Wenge Liu
  • Yi Cheng
  • Hao Wang
  • Jianheng Tang
  • Yafei Liu
  • Ruihui Zhao
  • Wenjie Li
  • Yefeng Zheng

With the rise of telemedicine, the task of developing Dialogue Systems for Medical Diagnosis (DSMD) has received much attention in recent years. Different from early researches that needed to rely on extra human resources and expertise to build the system, recent researches focused on how to build DSMD in a data-driven manner. However, the previous data-driven DSMD methods largely overlooked the system interpretability, which is critical for a medical application, and they also suffered from the data sparsity issue at the same time. In this paper, we explore how to bring interpretability to data-driven DSMD. Specifically, we propose a more interpretable decision process to implement the dialogue manager of DSMD by reasonably mimicking real doctors' inquiry logics, and we devise a model with highly transparent components to conduct the inference. Moreover, we collect a new DSMD dataset, which has a much larger scale, more diverse patterns, and is of higher quality than the existing ones. The experiments show that our method obtains 7. 7%, 10. 0%, 3. 0% absolute improvement in diagnosis accuracy respectively on three datasets, demonstrating the effectiveness of its rational decision process and model design. Our codes and the GMD-12 dataset are available at https: //github. com/lwgkzl/BR-Agent.

PDF Details DOI
v2026.09.13