Summary of Wasserstein Distributionally Robust Shallow Convex Neural Networks, by Julien Pallage and Antoine Lesage-landry
Wasserstein Distributionally Robust Shallow Convex Neural Networks
by Julien Pallage, Antoine Lesage-Landry
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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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 This paper proposes Wasserstein distributionally robust shallow convex neural networks (WaDiRo-SCNNs) to provide reliable nonlinear predictions when faced with corrupted or adverse datasets. The approach is based on a new convex training program for ReLU-based shallow neural networks, which allows the problem to be cast as an exact and tractable reformulation of its order-1 Wasserstein distributionally robust counterpart. The training procedure is conservative, has low stochasticity, and can be solved using open-source solvers, making it scalable to large industrial deployments. The paper also provides out-of-sample performance guarantees, shows how to enforce hard convex physical constraints in the training program, and proposes a mixed-integer convex post-training verification program to evaluate model stability. The goal is to make neural networks safer for critical applications, such as those in the energy sector. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes deep learning models more reliable by preventing them from being fooled by bad data. It creates a new type of neural network that can predict things accurately even when the training data is messy or corrupted. The approach uses special math to make sure the predictions are good, and it also checks the model’s stability afterwards. The goal is to use this technology in important fields like energy, where mistakes could have big consequences. |
Keywords
» Artificial intelligence » Deep learning » Neural network » Relu
