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
Large foundation models have become central to modern machine learning, with performance scaling predictably with model size and data. However, training and deploying such models i…
cs.LG2024
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…