Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound, Neural Scaling Law and Minimax Optimality.
Yiping LuHaoxuan ChenJianfeng LuLexing YingJose BlanchetPublished in: CoRR (2021)
Keyphrases
- machine learning
- partial differential equations
- worst case
- upper bound
- network architecture
- neural network
- generalization bounds
- learning algorithm
- machine learning algorithms
- learning machines
- numerical solution
- image denoising
- machine learning methods
- learning problems
- lower bound
- pattern recognition
- support vector machine
- knowledge acquisition
- level set
- data mining
- optimal solution
- real valued functions
- multiscale
- text mining
- boundary value problem
- feature selection
- image processing
- inductive learning
- reinforcement learning
- level set method
- energy functional
- supreme court
- bipartite ranking
- inductive bias
- fourth order
- numerical methods
- anisotropic diffusion
- associative memory
- computer science
- learning tasks
- data analysis
- knowledge representation
- dynamical systems
- supervised learning
- natural language processing
- denoising