9 citations · 16 across the 10 of their papers we have counts for
6 papers · 1 filter
BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting
Weiyan Wang, Xingjian Shi, Ruiqi Shu +10
In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose…
Scaling Laws for Floating Point Quantization Training
Xingwu Sun, Shuaipeng Li, Ruobing Xie +13
Low-precision training is considered an effective strategy for reducing both training and downstream inference costs. Previous scaling laws for precision mainly focus on integer qu…
Exploiting Student Parallelism for Efficient GPU Inference of BERT-like Models in Online Services
Weiyan Wang, Yilun Jin, Yiming Zhang +7
Due to high accuracy, BERT-like models have been widely adopted by text mining and web searching. However, large BERT-like models suffer from inefficient online inference, facing t…
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…
BeamVQ: Aligning Space-Time Forecasting Model via Self-training on Physics-aware Metrics
Hao Wu, Xingjian Shi, Ziyue Huang +6
Data-driven deep learning has emerged as the new paradigm to model complex physical space-time systems. These data-driven methods learn patterns by optimizing statistical metrics a…
Surge Phenomenon in Optimal Learning Rate and Batch Size Scaling
Shuaipeng Li, Penghao Zhao, Hailin Zhang +10
In current deep learning tasks, Adam style optimizers such as Adam, Adagrad, RMSProp, Adafactor, and Lion have been widely used as alternatives to SGD style optimizers. These optim…