Summary of Optimization Proxies Using Limited Labeled Data and Training Time — a Semi-supervised Bayesian Neural Network Approach, by Parikshit Pareek et al.
Optimization Proxies using Limited Labeled Data and Training Time – A Semi-Supervised Bayesian Neural Network Approach
by Parikshit Pareek, Abhijith Jayakumar, Kaarthik Sundar, Deepjyoti Deka, Sidhant Misra
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 semi-supervised Bayesian Neural Networks (BNNs) based optimization proxy addresses the limitations of standard deep neural network (DNN) based machine learning proxies in constrained optimization problems. By alternating between supervised and unsupervised learning steps, the BNN outperforms DNN architectures on non-convex constrained optimization problems from energy network operations, reducing expected maximum equality gaps by up to tenfold and halving inequality gaps. The BNN’s ability to provide posterior samples is also leveraged to construct practically meaningful probabilistic confidence bounds using a limited validation data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new approach to solving complex engineering problems. It uses a special kind of artificial intelligence called Bayesian Neural Networks (BNNs) to help find the best solution when there’s not much labeled data available. The BNN works by taking small steps towards finding the answer, first by trying to minimize a cost, and then by making sure that the solution is reasonable. This approach is better than other methods at solving problems from energy networks, where it can reduce errors by up to tenfold. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Optimization » Semi supervised » Supervised » Unsupervised
