We lifted 4 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| xuanqing94/NeuralSDE | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| downsample | Ran | xuanqing94/NeuralSDE/models/flow_net.py pointer only (licence: NOASSERTION) · get_code("46e8e9e9cfb1b872") |
| test_one_epoch | Ran | xuanqing94/NeuralSDE/train_xde.py pointer only (licence: NOASSERTION) · get_code("35867fe09f495a75") |
| train_one_epoch | Ran | xuanqing94/NeuralSDE/train_xde.py pointer only (licence: NOASSERTION) · get_code("527ceb9f828b115e") |
| upsample | Ran | xuanqing94/NeuralSDE/models/flow_net.py pointer only (licence: NOASSERTION) · get_code("3d1a78e0efa17a25") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural Ordinary Differential Equation (Neural ODE) has been proposed as a continuous approximation to the ResNet architecture. Some commonly used regularization mechanisms in discrete neural networks (e.g. dropout, Gaussian noise) are missing in current Neural ODE networks. In this paper, we propose a new continuous neural network framework called Neural Stochastic Differential Equation (Neural SDE) network, which naturally incorporates various commonly used regularization mechanisms based on random noise injection. Our framework can model various types of noise injection frequently used in discrete networks for regularization purpose, such as dropout and additive/multiplicative noise in each block. We provide theoretical analysis explaining the improved robustness of Neural SDE models against input perturbations/adversarial attacks. Furthermore, we demonstrate that the Neural SDE network can achieve better generalization than the Neural ODE and is more resistant to adversarial and non-adversarial input perturbations.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1906.02355")
get_code_for_paper("1906.02355")
have("1906.02355")
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