Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality.
Yiping LuHaoxuan ChenJianfeng LuLexing YingJose H. BlanchetPublished in: ICLR (2022)
Keyphrases
- machine learning
- partial differential equations
- worst case
- neural network
- numerical solution
- upper bound
- supervised learning
- generalization bounds
- machine learning methods
- pattern recognition
- data mining
- image denoising
- image enhancement
- machine learning algorithms
- lower bound
- network architecture
- image processing
- computer vision
- artificial intelligence
- estimation error
- learning machines
- anisotropic diffusion
- computational intelligence
- level set
- neural model
- learning tasks
- learning problems
- evaluation function
- legal reasoning
- knowledge acquisition
- error bounds
- model selection
- text mining
- multiscale
- text classification
- associative memory
- biologically inspired
- active learning
- data analysis
- computer science
- decision trees
- feature selection
- learning algorithm
- number of iterations required
- bipartite ranking