Kexin Chen

Orcid: 0000-0001-5212-0635

Affiliations:
  • Chinese University of Hong Kong, Department of Computer Science Engineering, Hong Kong


According to our database1, Kexin Chen authored at least 13 papers between 2021 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2025
VeriRAG: A Retrieval-Augmented Framework for Automated RTL Testability Repair.
CoRR, July, 2025

Cellular-X: An LLM-empowered Cellular Agent for Efficient Base Station Operations.
CoRR, April, 2025

The Dual-use Dilemma in LLMs: Do Empowering Ethical Capacities Make a Degraded Utility?
CoRR, January, 2025

Rephrase and Contrast: Fine-Tuning Language Models for Enhanced Understanding of Communication and Computer Networks.
Proceedings of the International Conference on Computing, Networking and Communications, 2025

2024
An Autonomous Large Language Model Agent for Chemical Literature Data Mining.
CoRR, 2024

Addressing Out-of-Distribution Challenges in Image Semantic Communication Systems with Multi-modal Large Language Models.
Proceedings of the 22nd International Symposium on Modeling and Optimization in Mobile, 2024

LLM for Complex Signal Processing in FPGA-based Software Defined Radios: A Case Study on FFT.
Proceedings of the 100th IEEE Vehicular Technology Conference, 2024

LLM-Assisted Multi-Teacher Continual Learning for Visual Question Answering in Robotic Surgery.
Proceedings of the IEEE International Conference on Robotics and Automation, 2024

2023
MetaRF: attention-based random forest for reaction yield prediction with a few trails.
J. Cheminformatics, December, 2023

Towards an Automatic AI Agent for Reaction Condition Recommendation in Chemical Synthesis.
CoRR, 2023

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following.
CoRR, 2023

2022
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails.
CoRR, 2022

2021
Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.
Briefings Bioinform., 2021


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