Richard Zemel, David Madras
We lifted 12 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| dmadras/nooch | canonical | 10 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| auc | Ran | dmadras/nooch/src/nooch.py code served (permissive licence) · get_code("fc57d2f52876c2cc") |
| create_cocostuff_category_maps | Ran | dmadras/nooch/src/attr_functions.py code served (permissive licence) · get_code("260bbff2b621dfd5") |
| define_labels | Ran | dmadras/nooch/src/attr_functions.py code served (permissive licence) · get_code("89fca942f26988da") |
| device | Ran | dmadras/nooch/src/utils/utils.py code served (permissive licence) · get_code("2ae3d74510d82d77") |
| evaluate | Ran | dmadras/nooch/src/nooch.py code served (permissive licence) · get_code("773a53ce9d82db41") |
| gong | Ran | dmadras/nooch/src/utils/utils.py code served (permissive licence) · get_code("75cb70ed73e3ccbd") |
| init_model | Ran | dmadras/nooch/src/utils/utils.py code served (permissive licence) · get_code("8369e57e3ee67024") |
| load_cocostuff_category_maps | Ran | dmadras/nooch/src/attr_functions.py code served (permissive licence) · get_code("cfb4459c28de8af3") |
| merge_cocostuff_jsons | Ran | dmadras/nooch/src/utils/cocostuff_loading_utils.py code served (permissive licence) · get_code("ce22cca93ae61f2c") |
| nll | Ran | dmadras/nooch/src/nooch.py code served (permissive licence) · get_code("b246bd5672f4ff91") |
| load_cocostuff | Not yet run | dmadras/nooch/src/utils/cocostuff_loading_utils.py code served (permissive licence) · get_code("48e5e80e1ebe503a") |
| prepare_cocostuff_minibatching | Not yet run | dmadras/nooch/src/utils/cocostuff_loading_utils.py code served (permissive licence) · get_code("07c4b71349f1cafd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deep learning systems frequently fail at out-of-context (OOC) prediction, the problem of making reliable predictions on uncommon or unusual inputs or subgroups of the training distribution. To this end, a number of benchmarks for measuring OOC performance have recently been introduced. In this work, we introduce a framework unifying the literature on OOC performance measurement, and demonstrate how rich auxiliary information can be leveraged to identify candidate sets of OOC examples in existing datasets. We present NOOCH: a suite of naturallyoccurring "challenge sets", and show how varying notions of context can be used to probe specific OOC failure modes. Experimentally, we explore the tradeoffs between various learning approaches on these challenge sets and demonstrate how the choices made in designing OOC benchmarks can yield varying conclusions.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2110.13223")
get_code_for_paper("2110.13223")
have("2110.13223")
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