21 citations · 29 across the 9 of their papers we have counts for
10 papers
Single-step Retrosynthesis via Reinforcement Learning from Forward Feedback
Zixun Lan, Zhenfu Liu, Yi Li +6
Single-step retrosynthesis is a one-to-many inverse problem, yet most models are trained and evaluated against a single recorded reactant set. Reference-based training gives no exp…
Chinese Labor Law Large Language Model Benchmark
Zixun Lan, Maochun Xu, Yifan Ren +7
Recent advances in large language models (LLMs) have led to substantial progress in domain-specific applications, particularly within the legal domain. However, general-purpose mod…
Retrosynthesis Prediction via Search in (Hyper) Graph
Zixun Lan, Binjie Hong, Jiajun Zhu +4
Predicting reactants from a specified core product stands as a fundamental challenge within organic synthesis, termed retrosynthesis prediction. Recently, semi-template-based metho…
Deep Reinforcement Learning for Quantitative Trading
Maochun Xu, Zixun Lan, Zheng Tao +2
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the domain of Quantitative Trading (QT) through the deployment of advanced algorithms capable of sifting thr…
Use neural networks to recognize students' handwritten letters and incorrect symbols
JiaJun Zhu, Zichuan Yang, Binjie Hong +6
Correcting students' multiple-choice answers is a repetitive and mechanical task that can be considered an image multi-classification task. Assuming possible options are 'abcd' and…
RCsearcher: Reaction Center Identification in Retrosynthesis via Deep Q-Learning
Zixun Lan, Zuo Zeng, Binjie Hong +2
The reaction center consists of atoms in the product whose local properties are not identical to the corresponding atoms in the reactants. Prior studies on reaction center identifi…
AEDNet: Adaptive Edge-Deleting Network For Subgraph Matching
Zixun Lan, Ye Ma, Limin Yu +2
Subgraph matching is to find all subgraphs in a data graph that are isomorphic to an existing query graph. Subgraph matching is an NP-hard problem, yet has found its applications i…
More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference
Zixun Lan, Binjie Hong, Ye Ma +1
Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability,…
Sub-GMN: The Neural Subgraph Matching Network Model
Zixun Lan, Limin Yu, Linglong Yuan +3
As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry an…
Global-aware Beam Search for Neural Abstractive Summarization
Ye Ma, Zixun Lan, Lu Zong +1
This study develops a calibrated beam-based algorithm with awareness of the global attention distribution for neural abstractive summarization, aiming to improve the local optimali…