From the 1 of 6 linked papers with an AI index.
6 papers
A Lightweight Foundation Model for Collider Physics with Multi-Domain Adaptation
Liangyu Wu, Qibin Liu, Alexander Yue +1
The paper introduces NEXUS, a lightweight autoencoder foundation model with ~3 M parameters that is pretrained on Large Hadron Collider track data and fine‑tuned for collider tasks…
Towards anomaly detection searches for new physics signatures including Higgs bosons with weakly supervised machine learning
Chi Lung Cheng, Julia Gonski, Runze Li +5
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detect…
Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs
P. Alex May, Qibin Liu, Julia Gonski +1
Computationally expensive, high-accuracy detector simulations are a major bottleneck for many particle physics experiments such as those at the Large Hadron Collider (LHC) as well…
HGQ-LUT: Fast LUT-Aware Training and Efficient Architectures for DNN Inference
Chang Sun, Zhiqiang Que, Bakhtiar Zadeh +4
Lookup-table (LUT) based neural networks can deliver ultra-low latency and excellent hardware efficiency on FPGAs by mapping arithmetic operations directly onto the logic primitive…
MEMO: Fine-grained Tensor Management For Ultra-long Context LLM Training
Pinxue Zhao, Hailin Zhang, Fangcheng Fu +9
Nowadays, Large Language Models (LLMs) have been trained using extended context lengths to foster more creative applications. However, long context training poses great challenges…
LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing
Xiaonan Nie, Qibin Liu, Fangcheng Fu +6
Larger transformer models always perform better on various tasks but require more costs to scale up the model size. To efficiently enlarge models, the mixture-of-experts (MoE) arch…