2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2026
Stable FP4 Training via Transposition-Invariant Block Quantization
Mehdi Rahimifar, Amin Darabi, Mehran Taghian Jazi +6
Reducing training precision is a key lever for improving the e ciency of large language model (LLM) training, but pushing beyond FP8 to 4-bit oating point (FP4) remains challenging…
cs.LG2026
HiFloat4 Format for Language Model Pre-training on Ascend NPUs
Mehran Taghian, Yunke Peng, Xing Huang +22
Training large foundation models at low numerical precision is one of the most promising directions for reducing the compute and memory cost of modern AI. Recent 4-bit floating-poi…
cs.LG2024★ 2 cited
FedLog: Personalized Federated Classification with Less Communication and More Flexibility
Haolin Yu, Guojun Zhang, Pascal Poupart
Federated representation learning (FRL) aims to learn personalized federated models with effective feature extraction from local data. FRL algorithms that share the majority of the…