We lifted 6 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| mahdihosseini/genprob | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| EVBMF | Ran | mahdihosseini/genprob/source/process.py code served (permissive licence) · get_code("b23cbcd80c7a4289") |
| EVBsigma2 | Ran | mahdihosseini/genprob/source/process.py code served (permissive licence) · get_code("e2f77eaf75568aa2") |
| agg | Ran | mahdihosseini/genprob/source/qualities.py code served (permissive licence) · get_code("6627b133bdc36d11") |
| all_aggs | Ran | mahdihosseini/genprob/source/qualities.py code served (permissive licence) · get_code("5d48a562d0dd2843") |
| phi0 | Ran | mahdihosseini/genprob/source/process.py code served (permissive licence) · get_code("2760d409b48cf23f") |
| sqrtlog | Not yet run | mahdihosseini/genprob/source/qualities.py code served (permissive licence) · get_code("90be6d8dd0c888ca") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Understanding the generalization behaviour of deep neural networks is a topic of recent interest that has driven the production of many studies, notably the development and evaluation of generalization "explainability" measures that quantify model generalization ability. Generalization measures have also proven useful in the development of powerful layer-wise model tuning and optimization algorithms, though these algorithms require specific kinds of generalization measures which can probe individual layers. The purpose of this paper is to explore the neglected subtopic of probeable generalization measures; to establish firm ground for further investigations, and to inspire and guide the development of novel model tuning and optimization algorithms. We evaluate and compare measures, demonstrating effectiveness and robustness across model variations, dataset complexities, training hyperparameters, and training stages. We also introduce a new dataset of trained models and performance metrics, GenProb, for testing generalization measures, model tuning algorithms and optimization algorithms.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.12259")
get_code_for_paper("2110.12259")
have("2110.12259")
Connect an agent — have() is free.