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Found 5 papers, 2 papers with code
How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression
Neural network representations of simple models, such as linear regression, are being studied increasingly to better understand the underlying principles of deep learning algorithms.
Generalizing Orthogonalization for Models with Non-linearities
The complexity of black-box algorithms can lead to various challenges, including the introduction of biases.
Smoothing the Edges: Smooth Optimization for Sparse Regularization using Hadamard Overparametrization
Additionally, our theory establishes results of independent interest regarding matching local minima for arbitrary, potentially unregularized, objectives.
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.
