EDBT 2026 Demo / reviewers in the wild / expert
Xiaoliang Fu
dblp:233/1869
· DBLP profile ↗
3ranked-venue papers
2as first author
2since 2021 · last 2026
0000-0003-2550-3600ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-author · 2 since 2021Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Reinforcement learning · 50% Language models and text generation · 33% Optimization for machine learning · 17% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation
chain-of-thought reasoning |
1.0 | 1 | 2026 | MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning · ACL (1) 2026 |
Machine learning › Optimization for machine learning
gradient clipping |
1.0 | 1 | 2026 | From łog π to π: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight · ACL (1) 2026 |
Natural language and speech › Language models and text generation
large language model training |
1.0 | 1 | 2026 | From łog π to π: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight · ACL (1) 2026 |
Machine learning › Reinforcement learning
policy optimization |
1.0 | 1 | 2026 | MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning · ACL (1) 2026 |
Machine learning › Reinforcement learning › reinforcement learning for NLP
reinforcement fine-tuning |
1.0 | 1 | 2026 | MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning · ACL (1) 2026 |
Machine learning › Reinforcement learning › policy optimization
sample-efficient policy optimization |
1.0 | 1 | 2026 | MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM Reasoning · ACL (1) 2026 |
Methods — techniques the papers use, named apart from their topics
signal reliability · 1.0probability mass · 1.0gradient utilization · 1.0bilateral decoupled decay · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | From łog π to π: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient WeightabstractXiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, Ke Zeng. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao 0001 |
ACL (1) | 1 |
| 2026 | MASPO: Unifying Gradient Utilization, Probability Mass, and Signal Reliability for Robust and Sample-Efficient LLM ReasoningabstractXiaoliang Fu, Jiaye Lin, Yangyi Fang, Binbin Zheng, Chaowen Hu, Zekai Shao, Cong Qin, Lu Pan, Ke Zeng, Xunliang Cai. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026. Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Zekai Shao 0001, Cong Qin |
ACL (1) | 1 |
| 2018 | Development of FPGA based Hardware-in-the-loop Simulator for RF Cavity ResonatorabstractThis paper presents implementation of a Field Programmable Gate Array (FPGA) emulator for the Low Level Radio Frequency (LLRF) Normal conducting cavity at TRIUMF. Testing waveforms such as phase and amplitude in the cavity on the real hardware can be both difficult and dangerous. To allow testing of the real LLRF system in real time and also to obtain shorter processing time we need to have an effective hardware in the loop simulator of the cavity. Radio frequency(RF) cavity is equivalent to an electrical RLC circuit which acts like a second order filter model, therefore it is possible to emulate the cavity with an analog filter. But when it comes to digital field, the finite word length effect has to be considered and processed carefully. The discrete model of the cavity designed as an IIR bandpass Filter. The SIMULINK and MATLAB model of the cavity and their implementation are presented. An Altera DE4 development board was selected as the emulator of the cavity. And the result of digital designing of the cavity model on FPGA is presented. Mahsa Keikha, Xiaoliang Fu, Mehrdad Moallem, Ken Fong |
IECON | 2 |