Summary of Do Stable Neural Networks Exist For Classification Problems? — a New View on Stability in Ai, by Z. N. D. Liu et al.
Do stable neural networks exist for classification problems? – A new view on stability in AI
by Z. N. D. Liu, A. C. Hansen
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Functional Analysis (math.FA)
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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 paper addresses the issue of stability in deep learning (DL) for classification tasks, introducing a novel stability measure called (f) to study the stability of discontinuous functions and their approximations. The traditional approach using the Lipschitz constant is inadequate, as it considers every classification function unstable despite basic classification functions being locally stable except at the decision boundary. The authors prove two approximation theorems: one guarantees a neural network (NN) can approximate a classification function up to an accuracy of , and another ensures the NN approximates the original function on points that are at least away from the decision boundary. This work aims to establish a rigorous theory for stability in DL, allowing for the development of stable networks for classification tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores the concept of stability in deep learning (DL) and how it relates to classification tasks. Traditionally, stability is measured using the Lipschitz constant, but this method has limitations when applied to classification functions. The authors introduce a new measure called (f) to better capture the stability of these functions. They then prove two theorems that demonstrate the existence of stable neural networks (NNs) for classification tasks. |
Keywords
* Artificial intelligence * Classification * Deep learning * Neural network
