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Found 5 papers, 2 papers with code
Multimodal Deep Learning
This book is the result of a seminar in which we reviewed multimodal approaches and attempted to create a solid overview of the field, starting with the current state-of-the-art approaches in the two subfields of Deep Learning individually.
Privacy-Preserving and Lossless Distributed Estimation of High-Dimensional Generalized Additive Mixed Models
In this paper, we propose an algorithm for a distributed, privacy-preserving, and lossless estimation of generalized additive mixed models (GAMM) using component-wise gradient boosting (CWB).
Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization
Componentwise boosting (CWB), also known as model-based boosting, is a variant of gradient boosting that builds on additive models as base learners to ensure interpretability.
Automatic Componentwise Boosting: An Interpretable AutoML System
Despite its restriction to an interpretable model space, our system is competitive in terms of predictive performance on most data sets while being more user-friendly and transparent.
Component-Wise Boosting of Targets for Multi-Output Prediction
One way to address this problem is the so called problem transformation method.
