822 citations
- University of California, DavisUS24 papers
- California Institute of TechnologyUS21 papers
- University of California, IrvineUS17 papers
- Lawrence Livermore National LaboratoryUS16 papers
- Lawrence Berkeley National LaboratoryUS14 papers
- Sichuan UniversityCN14 papers
- Southeast UniversityCN14 papers
- Leiden UniversityNL13 papers
- Northwestern UniversityUS13 papers
- University of California, Los AngelesUS12 papers
- Princeton UniversityUS11 papers
- University of California, BerkeleyUS11 papers
18 papers · 1 filter
Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data
Shangjie Du, Hui Wei, Dong Yoon Lee +2
This work introduces GraPhy, a graph-based, physics-guided learning framework for high-resolution and accurate air quality modeling in urban areas with limited monitoring data. Fin…
Towards VM Rescheduling Optimization Through Deep Reinforcement Learning
Xianzhong Ding, Yunkai Zhang, Binbin Chen +6
Modern industry-scale data centers need to manage a large number of virtual machines (VMs). Due to the continual creation and release of VMs, many small resource fragments are scat…
MARLP: Time-series Forecasting Control for Agricultural Managed Aquifer Recharge
Yuning Chen, Kang Yang, Zhiyu An +4
The rapid decline in groundwater around the world poses a significant challenge to sustainable agriculture. To address this issue, agricultural managed aquifer recharge (Ag-MAR) is…
Data Race Detection Using Large Language Models
Le Chen, Xianzhong Ding, Murali Emani +3
Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumv…
Learning Stochastic Dynamics with Statistics-Informed Neural Network
Yuanran Zhu, Yu-Hang Tang, Changho Kim
We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspir…
Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida +2
A recent line of research investigates how algorithms can be augmented with machine-learned predictions to overcome worst case lower bounds. This area has revealed interesting algo…