5 papers
Disentangled Parameter-Efficient Linear Model for Long-Term Time Series Forecasting
Yuang Zhao, Tianyu Li, Jiadong Chen +3
Long-term Time Series Forecasting (LTSF) is crucial across various domains, but complex deep models like Transformers are often prone to overfitting on extended sequences. Linear F…
Online Ensemble Transformer for Accurate Cloud Workload Forecasting in Predictive Auto-Scaling
Jiadong Chen, Xiao He, Hengyu Ye +4
In the swiftly evolving domain of cloud computing, the advent of serverless systems underscores the crucial need for predictive auto-scaling systems. This necessity arises to ensur…
Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services
Jiadong Chen, Hengyu Ye, Fuxin Jiang +4
Workload forecasting is pivotal in cloud service applications, such as auto-scaling and scheduling, with profound implications for operational efficiency. Although Transformer-base…
MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem
Fan Liu, Zherui Yang, Cancheng Liu +3
Mathematical modeling is a cornerstone of scientific discovery and engineering practice, enabling the translation of real-world problems into formal systems across domains such as…
MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection
Bokai Lin, Zihao Zeng, Zipeng Xiao +5
KV cache has become a de facto technique for the inference of large language models (LLMs), where tensors of shape (layer number, head number, sequence length, feature dimension) a…