2 citations · 4 across the 3 of their papers we have counts for
4 papers
Optimizing Effective Training Time for Large-Scale Recommendation Systems
Mingming Ding, Ruilin Chen, Yuzhen Huang +29
Lifecycle overhead silently consumes accelerator capacity across large-scale recommendation training fleets. Our largest recommendation workloads process tens of billions train- in…
Optimus: A Generic Operator-Level PyTorch Model Transformation Framework
Menglu Yu, Jiaqi Xu, Yuzhen Huang +19
In large-scale industrial applications, deep learning models that power recommendation and ranking have complex and diverse model architectures. These models are continuously devel…
Accelerating Neural Network Training: An Analysis of the AlgoPerf Competition
Priya Kasimbeg, Frank Schneider, Runa Eschenhagen +11
The goal of the AlgoPerf: Training Algorithms competition is to evaluate practical speed-ups in neural network training achieved solely by improving the underlying training algorit…
One-Step Diffusion Distillation via Deep Equilibrium Models
Zhengyang Geng, Ashwini Pokle, J. Zico Kolter
Diffusion models excel at producing high-quality samples but naively require hundreds of iterations, prompting multiple attempts to distill the generation process into a faster net…