Search Results for author:
Found 5 papers, 2 papers with code
Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting
In this paper, we propose to leverage a hybrid framework Mambaformer that internally combines Mamba for long-range dependency, and Transformer for short range dependency, for long-short range forecasting.
Interpretable Modeling of Deep Reinforcement Learning Driven Scheduling
In this work, we present a framework called IRL (Interpretable Reinforcement Learning) to address the issue of interpretability of DRL scheduling.
DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling
For decades, system administrators have been striving to design and tune cluster scheduling policies to improve the performance of high performance computing (HPC) systems.
Deep Reinforcement Agent for Scheduling in HPC
Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads.
Performance and Power Modeling and Prediction Using MuMMI and Ten Machine Learning Methods
In this paper, we use modeling and prediction tool MuMMI (Multiple Metrics Modeling Infrastructure) and ten machine learning methods to model and predict performance and power and compare their prediction error rates.
