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Found 4 papers, 1 papers with code
Transfer Learning for Bayesian HPO with End-to-End Meta-Features
As a result, our novel DKLM can learn contextualized dataset-specific similarity representations for hyperparameter configurations.
Do We Really Need Deep Learning Models for Time Series Forecasting?
In this paper, we report the results of prominent deep learning models with respect to a well-known machine learning baseline, a Gradient Boosting Regression Tree (GBRT) model.
Zero-shot Transfer Learning for Gray-box Hyper-parameter Optimization
Zero-shot hyper-parameter optimization refers to the process of selecting hyper- parameter configurations that are expected to perform well for a given dataset upfront, without access to any observations of the losses of the target response.
Handwritten Amharic Character Recognition Using a Convolutional Neural Network
The model was further enhanced using multi-task learning from the relationships of the characters.
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