Summary of Scalable Subsampling Inference For Deep Neural Networks, by Kejin Wu and Dimitris N. Politis
Scalable Subsampling Inference for Deep Neural Networks
by Kejin Wu, Dimitris N. Politis
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Computation (stat.CO)
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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 A non-asymptotic error bound is developed for fully connected deep neural networks (DNNs) with ReLU activation functions, enabling accurate estimation of regression models. This paper improves upon current bounds by leveraging recent advancements in DNN approximation abilities. Moreover, a scalable subsampling technique, dubbed subagged' DNN estimator, is introduced, offering computational efficiency without sacrificing accuracy for both estimation and prediction tasks. Beyond point estimation/prediction, confidence and prediction intervals are proposed based on the subagged DNN estimator. These intervals are asymptotically valid and perform well in finite samples. The scalable subsampling DNN estimator provides a comprehensive solution for statistical inference, combining computational efficiency, accurate point estimation/prediction, and the construction of practically useful confidence and prediction intervals.</td> </tr> <tr> <td>Low</td> <td>GrooveSquid.com (original content)</td> <td><strong>Low Difficulty Summary</strong><br>This paper is about improving how we use deep neural networks (DNNs) to make predictions. The main goal is to find a way to make DNNs more efficient while still getting good results. To do this, researchers developed a new method calledsubagged’ that helps reduce the amount of computation needed without sacrificing accuracy. This is important because it allows us to make predictions and get confidence intervals (which tell us how likely we are correct) in a more efficient way. The new method also works well when dealing with real-world data, which makes it a useful tool for making predictions. |
Keywords
» Artificial intelligence » Inference » Regression » Relu
