About ScalingOpt
We are dedicated to advancing the frontier of optimization in large-scale machine learning through open collaboration, rigorous benchmarking, and accessible knowledge sharing.
Our Mission
Optimization lies at the heart of modern artificial intelligence. As models grow exponentially in size and complexity, the choice of optimization algorithm becomes increasingly critical for efficient training and convergence.
ScalingOpt was founded with a singular mission: to democratize access to state-of-the-art optimization techniques. We believe that by providing a centralized, standardized, and high-quality repository of algorithms, benchmarks, and educational resources, we can accelerate progress in the field of deep learning.
Our Team
Meet the members behind ScalingOpt.
Team member information is continuously updated. We welcome email applications for collaboration.
Research & Development
We operate at the intersection of academic research and engineering practice.
New Algorithm
We develop innovative optimization algorithms to address emerging challenges in training large-scale models, focusing on efficiency and stability.
Algorithm Analysis
We conduct in-depth analysis of optimization dynamics, convergence properties, and hyperparameter sensitivity to provide actionable insights for practitioners.
Fair Benchmarking
Our benchmarking suite ensures fair comparisons by controlling confounding variables and testing across diverse architectures and datasets.
Knowledge Sharing
ScalingOpt is dedicated to optimization-centric knowledge sharing and research.
Tutorials
Comprehensive guides and educational resources to help you master optimization concepts and techniques.
Blogs
Insights, updates, and deep dives into the latest research and trends in optimization.
Tech Reports
We provide technical reports on the latest optimization research, algorithm analysis, and benchmarking results.