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DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints
Dynamic difficulty adjustment ($DDA$) is a process of automatically changing a game difficulty for the optimization of user experience.
Generalized Quantile Loss for Deep Neural Networks
This note presents a simple way to add a count (or quantile) constraint to a regression neural net, such that given $n$ samples in the training set it guarantees that the prediction of $m<n$ samples will be larger than the actual value (the label).
Fast and Accurate Gaussian Kernel Ridge Regression Using Matrix Decompositions for Preconditioning
This paper presents a method for building a preconditioner for a kernel ridge regression problem, where the preconditioner is not only effective in its ability to reduce the condition number substantially, but also efficient in its application in terms of computational cost and memory consumption.
