Canatar et al. propose a predictive theory of generalization in kernel regression applicable to real data. This theory explains various generalization phenomena observed in wide neural networks, which admit a kernel limit and generalize well despite being overparameterized.
- Abdulkadir Canatar
- Blake Bordelon
- Cengiz Pehlevan
