Search Results for author:
Found 3 papers, 2 papers with code
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization
This is particularly useful in non-convex regularization, where finding global solutions is NP-hard and local minima often generalize well.
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities
We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
