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$Ae^2I$: A Double Autoencoder for Imputation of Missing Values
The most common strategy of imputing missing values in a table is to study either the column-column relationship or the row-row relationship of the data table, then use the relationship to impute the missing values based on the non-missing values from other columns of the same row, or from the other rows of the same column.
Data-aware customization of activation functions reduces neural network error
Activation functions play critical roles in neural networks, yet current off-the-shelf neural networks pay little attention to the specific choice of activation functions used.
Predicting Material Properties Using a 3D Graph Neural Network with Invariant Local Descriptors
Accurate prediction of physical properties is critical for discovering and designing novel materials.
A Use of Even Activation Functions in Neural Networks
The key to success is to effectively integrate existing knowledge or hypotheses about the underlying structure of the data into the architecture of deep learning models to guide machine learning.
Convex Regression in Multidimensions: Suboptimality of Least Squares Estimators
The least squares estimator (LSE) is shown to be suboptimal in squared error loss in the usual nonparametric regression model with Gaussian errors for $d \geq 5$ for each of the following families of functions: (i) convex functions supported on a polytope (in fixed design), (ii) bounded convex functions supported on a polytope (in random design), and (iii) convex Lipschitz functions supported on any convex domain (in random design).
