We lifted 1 functions out of this paper's own repositories and ran 0 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 | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| test | Not yet run | this paper's copy was not recorded; identical code first harvested from woodyx218/opacus_global_clipping pointer only · get_code("0084ec1c2df6dc92") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data. In practice, this has been mostly an afterthought, with privacy-preserving models obtained by re-running training with a different optimizer, but using the model architectures that already performed well in a non-privacy-preserving setting. This approach leads to less than ideal privacy/utility tradeoffs, as we show here. Instead, we propose that model architectures are chosen ab initio explicitly for privacy-preserving training. To provide guarantees under the gold standard of differential privacy, one must bound as strictly as possible how individual training points can possibly affect model updates. In this paper, we are the first to observe that the choice of activation function is central to bounding the sensitivity of privacy-preserving deep learning. We demonstrate analytically and experimentally how a general family of bounded activation functions, the tempered sigmoids, consistently outperform unbounded activation functions like ReLU. Using this paradigm, we achieve new state-of-the-art accuracy on MNIST, FashionMNIST, and CIFAR10 without any modification of the learning procedure fundamentals or differential privacy analysis.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2007.14191")
get_code_for_paper("2007.14191")
have("2007.14191")
Connect an agent — have() is free.