Summary of Derivative-based Regularization For Regression, by Enrico Lopedoto et al.
Derivative-based regularization for regression
by Enrico Lopedoto, Maksim Shekhunov, Vitaly Aksenov, Kizito Salako, Tillman Weyde
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach introduces a novel regularization method called DLoss for multivariable regression problems. DLoss penalizes differences between model derivatives and estimated data derivatives from training data. The goal is to align the model with the data, not just target values but also derivatives involved. Estimated data derivatives are obtained by selecting 2-tuples of input-value pairs from training data using nearest neighbour or random selection. Experiments on synthetic and real datasets demonstrate that adding DLoss with different weights improves performance, achieving better rank with respect to MSE on validation sets compared to no regularization, L2 regularization, and Dropout. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists created a new way to make machine learning models better at predicting relationships between multiple variables. They developed a technique called DLoss that helps the model match what’s happening in the data it’s being trained on. To do this, they compared the model’s predictions with the actual values from the training data and adjusted the model to fit. The results showed that using DLoss can improve how well the model performs, especially when compared to other methods. |
Keywords
» Artificial intelligence » Dropout » Machine learning » Mse » Regression » Regularization
