Association·arcadia
Grid search tuning hyperparameters for training
Optimization of latent space size, hidden layer size, and noise level by grid search for dataset-specific model training.
Confidence
100%
active
Evidence Quote
“For each dataset, we tuned the latent space size, hidden layer size, and noise amount using grid search.”
Relationship
Model training procedure optimizes Model hyperparameters
Connections (1)
Evidence
“Reference discussing the influence of nonlinear signals on prediction performance”
Heil BJ et al. (2022). The Effects of Nonlinear Signal on Expression-Based Prediction Performance doi:10.1101/2022.06.22.497194 ↗
“Reference evaluating limitations of deep learning predictions for gene perturbation effects”
Ahlmann-Eltze C et al. (2024). Deep learning-based predictions of gene perturbation effects do not yet outperform simple linear baselines doi:10.1101/2024.09.16.613342 ↗