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Found 8 papers, 0 papers with code
The Value of Chess Squares
We propose a neural network-based approach to calculate the value of a chess square-piece combination.
Feature Selection for Personalized Policy Analysis
In particular, our method is able to capture policy effect heterogeneity both within and across subgroups of the population defined by observable characteristics.
Gambits: Theory and Evidence
This allows us to calculate the $Q$-values of a Gambit where material (usually a pawn) is sacrificed for dynamic play.
Bayesian Inference for Gamma Models
We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions.
Deep Learning: Computational Aspects
In this article we review computational aspects of Deep Learning (DL).
Posterior Concentration for Sparse Deep Learning
As an aside, we show that SS-DL does not overfit in the sense that the posterior concentrates on smaller networks with fewer (up to the optimal number of) nodes and links.
Sparse Regularization in Marketing and Economics
Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution.
Deep Learning: A Bayesian Perspective
Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction.
