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Found 4 papers, 2 papers with code
AdaCap: Adaptive Capacity control for Feed-Forward Neural Networks
AdaCap is the combination of two novel ingredients, the Muddling labels for Regularization (MLR) loss and the Tikhonov operator training scheme.
Muddling Label Regularization: Deep Learning for Tabular Datasets
Deep Learning (DL) is considered the state-of-the-art in computer vision, speech recognition and natural language processing.
Muddling Labels for Regularization, a novel approach to generalization
The main goal of this paper is to introduce a novel approach to achieve generalization without any data splitting, which is based on a new risk measure which directly quantifies a model's tendency to overfit.
Optimizing generalization on the train set: a novel gradient-based framework to train parameters and hyperparameters simultaneously
We observe in our experiments a significantly smaller runtime for our methods as compared to benchmark methods for equivalent prediction score.
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