Tom Goldstein, Hao Li, Christoph Studer, Gavin Taylor, Zheng Xu
We lifted 8 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| marcellodebernardi/loss-landscapes | — | 1 of 2 |
| StephenThacker/Visualiation-of-Loss-Function | — | 1 of 1 |
| activatedgeek/function-space-map | — | 1 of 1 |
| okn-yu/Visualizing-the-Loss-Landscape-of-Neural-Nets | — | 0 of 2 |
| Westlake-AI/openmixup | — | 0 of 1 |
| JoelNiklaus/loss_landscape | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| ModelParameters | Ran | marcellodebernardi/loss-landscapes/loss_landscapes/model_interface/model_parameters.py code served (permissive licence) · get_code("de98a260f327adb1") |
| NormalizeVector | Ran | StephenThacker/Visualiation-of-Loss-Function/PlotLossFunction.py pointer only (licence: NONE) · get_code("ea488312c43ec2de") |
| perturb_params | Ran | activatedgeek/function-space-map/experiments/evaluate_landscape.py code served (permissive licence) · get_code("d50731bae3d21b5a") |
| filter_normalize | Not yet run | marcellodebernardi/loss-landscapes/loss_landscapes/model_interface/model_parameters.py code served (permissive licence) · get_code("a58cf2b132df8df9") |
| normalize_direction | Not yet run | Westlake-AI/openmixup/openmixup/utils/loss_landscape_utils.py code served (permissive licence) · get_code("465e856d3ec6e818") |
| normalize_direction | Not yet run | JoelNiklaus/loss_landscape/net_plotter.py code served (permissive licence) · get_code("8fda4d9878d66f73") |
| normalize_direction | Not yet run | okn-yu/Visualizing-the-Loss-Landscape-of-Neural-Nets/src/directions.py pointer only (licence: NONE) · get_code("47f610cf6e37def7") |
| normalize_directions_for_weights | Not yet run | okn-yu/Visualizing-the-Loss-Landscape-of-Neural-Nets/src/directions.py pointer only (licence: NONE) · get_code("89b9484af736d895") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural network training relies on our ability to find "good" minimizers of highly non-convex loss functions. It is well-known that certain network architecture designs (e.g., skip connections) produce loss functions that train easier, and wellchosen training parameters (batch size, learning rate, optimizer) produce minimizers that generalize better. However, the reasons for these differences, and their effects on the underlying loss landscape, are not well understood. In this paper, we explore the structure of neural loss functions, and the effect of loss landscapes on generalization, using a range of visualization methods. First, we introduce a simple "filter normalization" method that helps us visualize loss function curvature and make meaningful side-by-side comparisons between loss functions. Then, using a variety of visualizations, we explore how network architecture affects the loss landscape, and how training parameters affect the shape of minimizers.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1712.09913")
get_code_for_paper("1712.09913")
have("1712.09913")
Connect an agent — have() is free.