Scalable Polyhedral Verification of Recurrent Neural Networks
Creators
- 1. ETH Zürich
- 2. VMWare Research & UIUC
- 3. Hitachi Power Grids Research
Description
We present a scalable and precise verifier for recurrent neural networks, called Prover. Prover relies on two novel ideas: (i) a method to compute a set of polyhedral abstractions for the non-convex and non-linear recurrent update functions by combining sampling, optimization, and Fermat’s theorem, and (ii) a gradient descent based algorithm for abstraction refinement guided by the certification problem that combines multiple abstractions for each neuron. Using Prover, we present the first study of certifying a non-trivial use case of recurrent neural networks, namely speech classification. To achieve this, we additionally develop custom abstractions for the non-linear speech preprocessing pipeline. Our evaluation shows that Prover successfully verifies several challenging recurrent models in computer vision and speech classification beyond the reach of prior work.
Please follow the `readme.md` located in the home directory. The system requires:
Gurobi optimizer licence - please refer to https://www.gurobi.com/academia/academic-program-and-licenses/
GPU available environment (not necessary but highly recommended)
Preliminary information for the VM:
1. List of essential files and directory
* /home/cav2021-paper187/readme.md: includes detailed instructions about how to prepare and run the artifact.
* /home/cav2021-paper187/CAV2021_paper_187.pdf: the accepted paper pdf.
* /home/cav2021-paper187/artifact/: the artifact codes and necessaries.
2. Recommended resource allocaiton
* Memory: 4GB
* Disk: 20GB because of the dataset
* GPU available environment
3. Credentials
* ID: cav2021-paper187
* PW: 2021-187-prover
Files
Files
(2.4 GB)
Name | Size | Download all |
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md5:1877ebabf5191d78199ebfd5411a0ee3
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2.4 GB | Download |