Zonotope Domains for Lagrangian Neural Network Verification
Authors: Matt Jordan, Jonathan Hayase, Alex Dimakis, Sewoong Oh
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply Zono Dual to a variety of networks trained on MNIST and CIFAR-10 and demonstrate that Zono Dual outperforms the linear programming relaxation in both tightness and runtime, and yields a tighter bounding algorithm than the prior dual approaches. |
| Researcher Affiliation | Academia | Matt Jordan UT Austin mjordan@cs.utexas.edu Jonathan Hayase University of Washington jhayase@cs.washington.edu Alexandros G. Dimakis UT Austin dimakis@austin.utexas.edu Sewoong Oh University of Washington sewoong@cs.washington.edu |
| Pseudocode | No | The paper describes the algorithm steps in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Github Repo: https://github.com/revbucket/dual-verification |
| Open Datasets | Yes | We apply Zono Dual to a variety of networks trained on MNIST and CIFAR-10 |
| Dataset Splits | Yes | Figure 1 (left) contains the distribution of reported bounds over every correctly-classified example in the MNIST validation set. |
| Hardware Specification | No | The paper states it is 'amenable to GPU acceleration' but does not specify exact GPU models, CPU types, or other detailed hardware specifications in the main text. |
| Software Dependencies | Yes | Gurobi Optimization, LLC. Gurobi Optimizer Reference Manual, 2021. URL https://www. gurobi.com. |
| Experiment Setup | Yes | We then decompose each zonotope into 2-dimensional partitions, using the similarity heuristic for fully-connected networks, and the spatial heuristic for convolutional networks. Then we perform 1000 iterations of the Adam optimizer during the dual ascent procedure. |