Math Optimization
Hi! Welcome to the optimization track on Datafest! Here we will discuss both theory and practice of modern optimization. We say really modern because the results and methods explained in the section are used either to conduct bleeding-edge research or train neural networks faster and larger.
In this section, You’ll know about:
- Automatic Evaluation and Tuning System for Clustering Algorithms from Sergey Muravyov
- Structural Optimization for Composite Models with Evolutionary Algorithms from Nikolay Nikitin
- NVIDIA Tricks for Training Large Neural Nets with AMP/TF32 from Denis Timonin
- Riemannian Optimization from Maxim Kochurov
- Riemannian Optimization for Quantum Technologies by Ilia Luchnikov and Alexander Ryzhov
- Integrals in Extremely Large Dimensions from Yury Maximov
- Polynomial Optimization from Olga Kuryatnikov
Track program
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