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Most important theoretical result
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Neural Network Verification as Piecewise Linear Optimization
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- 1 Intro
- 2 Neural network verification
- 3 Key insights and approach
- 4 Optimization over a trained neural network
- 5 Fitting unknown functions to make predictions
- 6 Application: Deep reinforcement learning
- 7 Application: Designing DNA for protein binding
- 8 Neural networks in one slide
- 9 Most important theoretical result
- 10 MIP formulations for a single ReLU neuron
- 11 MIP formulation strength
- 12 Formulations for convex PWL functions
- 13 Network 1: Small network standard training
- 14 Propagation algorithms
- 15 Computational results
- 16 Extensions: Binarized and quantized networks
