The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
The document discusses hyperparameter optimization in machine learning models. It introduces various hyperparameters that can affect model performance, and notes that as models become more complex, the number of hyperparameters increases, making manual tuning difficult. It formulates hyperparameter optimization as a black-box optimization problem to minimize validation loss and discusses challenges like high function evaluation costs and lack of gradient information.
Computing for Isogeny Kernel Problem by Groebner BasisYasu Math
Today, Tani's Claw finding algorithm is the fastest method of isogeny kernel problem. However, We don't use the property of elliptic curves and isogeny to solve the problem by Tani's algorithm. We suggest new method of computing for isogeny kernel problem by Velu's formula and Groebner basis.