Summary of Approximation Bounds For Recurrent Neural Networks with Application to Regression, by Yuling Jiao et al.
Approximation Bounds for Recurrent Neural Networks with Application to Regression
by Yuling Jiao, Yang Wang, Bokai Yan
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 abstract discusses the approximation capabilities of deep ReLU recurrent neural networks (RNNs) and their convergence properties in nonparametric least squares regression. The authors derive upper bounds on the approximation error for Hölder smooth functions, demonstrating that RNNs can simultaneously approximate a sequence of past-dependent Hölder functions. This work also provides non-asymptotic upper bounds for the prediction error of the empirical risk minimizer in regression problems, achieving optimal rates under both exponentially β-mixing and i.i.d. data assumptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well deep ReLU recurrent neural networks (RNNs) can imitate certain types of functions. The researchers show that RNNs are really good at approximating functions that depend only on past information, which is helpful for tasks like predicting what will happen next based on what has happened before. They also provide rules for how well an RNN will do in a specific problem, and these rules are better than what others have come up with so far. |
Keywords
» Artificial intelligence » Regression » Relu » Rnn
