Enhancing Performance and Scalability of Large-Scale Recommendation Systems with Jagged Flash Attention

no code implementations • 19 Sep 2024 • Rengan Xu, Junjie Yang, Yifan Xu, Hong Li, Xing Liu, Devashish Shankar, Haoci Zhang, Meng Liu, Boyang Li, Yuxi Hu, Mingwei Tang, Zehua Zhang, Tunhou Zhang, Dai Li, Sijia Chen, Gian-Paolo Musumeci, Jiaqi Zhai, Bill Zhu, Hong Yan, Srihari Reddy

In production models, we observe 10% QPS improvement and 18% memory savings, enabling us to scale our recommendation systems with longer features and more complex architectures.

Recommendation Systems

Async Learned User Embeddings for Ads Delivery Optimization

no code implementations • 9 Jun 2024 • Mingwei Tang, Meng Liu, Hong Li, Junjie Yang, Chenglin Wei, Boyang Li, Dai Li, Rengan Xu, Yifan Xu, Zehua Zhang, Xiangyu Wang, Linfeng Liu, Yuelei Xie, Chengye Liu, Labib Fawaz, Li Li, Hongnan Wang, Bill Zhu, Sri Reddy

In recommendation systems, high-quality user embeddings can capture subtle preferences, enable precise similarity calculations, and adapt to changing preferences over time to maintain relevance.

Graph Learning Recommendation Systems +1

Fitting stochastic epidemic models to gene genealogies using linear noise approximation

1 code implementation • 24 Feb 2019 • Mingwei Tang, Gytis Dudas, Trevor Bedford, Vladimir N. Minin

We propose a Bayesian model that combines phylodynamic inference and stochastic epidemic models, and achieves computational tractability by using a linear noise approximation (LNA) --- a technique that allows us to approximate probability densities of stochastic epidemic model trajectories.