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1337. SDP Relaxations for Training ReLU Activation Neural Networks
Invited abstract in session MC-4: New Trends in Generative Adversarial Networks and Deep Neural Networks , stream Recent Advancements in AI .
Monday, 12:30-14:00Room: 1001 (building: 202)
Authors (first author is the speaker)
| 1. | Karthik Prakhya
|
| Umeå University | |
| 2. | TOLGA BIRDAL
|
| Computing, Imperial College London | |
| 3. | Alp Yurtsever
|
| Umeå University |
Abstract
Solving non-convex optimization problems is crucial for training machine learning models, including neural networks. However, non-convexity often leads to less reliable and less robust neural networks with unclear inner workings. While convex formulations have been used for verifying neural network robustness, their application to training neural networks remains relatively unexplored. In this work, we propose a semidefinite programming relaxation for training two-layer ReLU networks in a lifted space, which can be solved in polynomial time. Numerical experiments demonstrate that this SDP formulation provides reasonably tight lower bounds on the training objective across various prediction and classification tasks.
Keywords
- Programming, Semidefinite
- Machine Learning
- Convex Optimization
Status: accepted
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