32 citations · 146 across the 11 of their papers we have counts for
11 papers
Learning to Generalize Provably in Learning to Optimize
Junjie Yang, Tianlong Chen, Mingkang Zhu +4
Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches. However, current L2O methods often suffer from poor…
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation
Junjie Yang, Xuxi Chen, Tianlong Chen +2
Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by ``overfitting" specific task type, leadi…
APT-36K: A Large-scale Benchmark for Animal Pose Estimation and Tracking
Yuxiang Yang, Junjie Yang, Yufei Xu +3
Animal pose estimation and tracking (APT) is a fundamental task for detecting and tracking animal keypoints from a sequence of video frames. Previous animal-related datasets focus…
Provably Faster Algorithms for Bilevel Optimization
Junjie Yang, Kaiyi Ji, Yingbin Liang
Bilevel optimization has been widely applied in many important machine learning applications such as hyperparameter optimization and meta-learning. Recently, several momentum-based…
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
Dheevatsa Mudigere, Yuchen Hao, Jianyu Huang +50
Deep learning recommendation models (DLRMs) are used across many business-critical services at Facebook and are the single largest AI application in terms of infrastructure demand…
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study
Cheng Chen, Junjie Yang, Yi Zhou
Modern deep neural network (DNN) trainings utilize various training techniques, e.g., nonlinear activation functions, batch normalization, skip-connections, etc. Despite their effe…