We lifted 6 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| copy not recorded | — | 1 of 3 |
| sungyoon-lee/bcp | pwc_unofficial | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| catNonNullErrors | Ran | this paper's copy was not recorded; identical code first harvested from eth-sri/diffai pointer only · get_code("f8f37cd8a52870c1") |
| argparser | Not yet run | sungyoon-lee/bcp/utils.py code served (permissive licence) · get_code("846b8ce1a0cef2b3") |
| creluBoxy | Not yet run | this paper's copy was not recorded; identical code first harvested from eth-sri/diffai pointer only · get_code("a49c0dd16152c346") |
| creluBoxySound | Not yet run | this paper's copy was not recorded; identical code first harvested from eth-sri/diffai pointer only · get_code("c42ef7b9012126aa") |
| model_cnn_4layer | Not yet run | sungyoon-lee/bcp/utils.py code served (permissive licence) · get_code("fb45ae0df896c8db") |
| select_model | Not yet run | sungyoon-lee/bcp/utils.py code served (permissive licence) · get_code("bb465636abefa4b4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often result in difficult optimization procedures that remain hard to scale to larger networks. Through a comprehensive analysis, we show how a simple bounding technique, interval bound propagation (IBP), can be exploited to train large provably robust neural networks that beat the state-of-the-art in verified accuracy. While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and clever hyper-parameter schedule allow the network to adapt such that the IBP bound is tight. This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN. It also allows us to train the largest model to be verified beyond vacuous bounds on a downscaled version of ImageNet.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1810.12715")
get_code_for_paper("1810.12715")
have("1810.12715")
Connect an agent — have() is free.