We lifted 3 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 |
|---|---|---|
| skander-karkar/LAP | canonical | 1 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| create_autoencoder | Ran | skander-karkar/LAP/utils.py pointer only (licence: NONE) · get_code("65f72dfc4a9db1bf") |
| create_classifier | Not yet run | skander-karkar/LAP/utils.py pointer only (licence: NONE) · get_code("b3f58f8ed60a82d6") |
| topkaccuracy | Not yet run | skander-karkar/LAP/utils.py pointer only (licence: NONE) · get_code("7d5c66f6d4b651d5") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural networks have been achieving high generalization performance on many tasks despite being highly over-parameterized. Since classical statistical learning theory struggles to explain this behavior, much effort has recently been focused on uncovering the mechanisms behind it, in the hope of developing a more adequate theoretical framework and having a better control over the trained models. In this work, we adopt an alternate perspective, viewing the neural network as a dynamical system displacing input particles over time. We conduct a series of experiments and, by analyzing the network's behavior through its displacements, we show the presence of a low kinetic energy displacement bias in the transport map of the network, and link this bias with generalization performance. From this observation, we reformulate the learning problem as follows: finding neural networks which solve the task while transporting the data as efficiently as possible. This offers a novel formulation of the learning problem which allows us to provide regularity results for the solution network, based on Optimal Transport theory. From a practical viewpoint, this allows us to propose a new learning algorithm, which automatically adapts to the complexity of the given task, and leads to networks with a high generalization ability even in low data regimes.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2009.08372")
get_code_for_paper("2009.08372")
have("2009.08372")
Connect an agent — have() is free.