3 papers
cs.LG2026
KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta
Gang Liao, Hongsen Qin, Ying Wang +36
Making deep learning recommendation model (DLRM) training and inference fast and efficient is important. However, this presents three key system challenges - model architecture div…
cs.DB2026
Experience Graphs: The Data Foundation for Self-Improving Agents
Gang Liao, Yujia He, Abdullah Ozturk +22
The database community has repeatedly advanced the state of the art by recognizing that new workloads demand new system architectures. We argue that long-horizon agentic tasks -- c…
cs.IR2024
ERCache: An Efficient and Reliable Caching Framework for Large-Scale User Representations in Meta's Ads System
Fang Zhou, Yaning Huang, Dong Liang +21
The increasing complexity of deep learning models used for calculating user representations presents significant challenges, particularly with limited computational resources and s…