Shengyu Ye

Orcid: 0009-0004-7688-8517

According to our database1, Shengyu Ye authored at least 12 papers between 2024 and 2026.

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

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Channel Estimation for 6G Near-Field Wireless Communications: A Comprehensive Survey.
IEEE Commun. Surv. Tutorials, 2026

LUT-LLM: Efficient Language Model Inference with Memory-based Computations on FPGAs.
Proceedings of the 34th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2026

Themis: Automated Constraint-Aware Test Synthesis Framework for Code Reinforcement Learning.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
LUT-LLM: Efficient Large Language Model Inference with Memory-based Computations on FPGAs.
CoRR, November, 2025

Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs.
CoRR, June, 2025

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator.
CoRR, January, 2025

Iterative ESPRIT Algorithm for DoA Estimation in Integrated OAM Radar-Communication Systems.
Proceedings of the 102nd IEEE Vehicular Technology Conference, 2025

CursorCore: Assist Programming through Aligning Anything.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

LUT-DLA: Lookup Table as Efficient Extreme Low-Bit Deep Learning Accelerator.
Proceedings of the IEEE International Symposium on High Performance Computer Architecture, 2025

VERSE: Verification-based Self-Play for Code Instructions.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
VPTQ: Extreme Low-bit Vector Post-Training Quantization for Large Language Models.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Optimizing Code Retrieval: High-Quality and Scalable Dataset Annotation through Large Language Models.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024


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