Jingyang Zhou, Alex Williams, Josue Nassar, Lyndon Duong, Jules Berman, Jeroen Olieslagers
We lifted 7 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| ahwillia/netrep | canonical | 7 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| align | Ran | ahwillia/netrep/netrep/utils.py code served (permissive licence) · get_code("e3a13a9dbac537a1") |
| check_equal_shapes | Ran | ahwillia/netrep/netrep/validation.py code served (permissive licence) · get_code("d93cab147768102d") |
| cross_distances | Ran | ahwillia/netrep/netrep/multiset.py code served (permissive licence) · get_code("8c975d8811c0e60e") |
| pairwise_distances | Ran | ahwillia/netrep/netrep/multiset.py code served (permissive licence) · get_code("91093e5cd3d80b03") |
| posdefsqrt | Ran | ahwillia/netrep/netrep/utils.py code served (permissive licence) · get_code("27a5bb3f60533931") |
| split | Ran | ahwillia/netrep/netrep/metrics/stochastic_process.py code served (permissive licence) · get_code("1e8f7903c244e8ab") |
| sq_bures_metric_slow | Ran | ahwillia/netrep/netrep/utils.py code served (permissive licence) · get_code("66514727b6e365f6") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Quantifying similarity between neural representations-e.g. hidden layer activation vectors-is a perennial problem in deep learning and neuroscience research. Existing methods compare deterministic responses (e.g. artificial networks that lack stochastic layers) or averaged responses (e.g., trial-averaged firing rates in biological data). However, these measures of deterministic representational similarity ignore the scale and geometric structure of noise, both of which play important roles in neural computation. To rectify this, we generalize previously proposed shape metrics (Williams et al., 2021) to quantify differences in stochastic representations. These new distances satisfy the triangle inequality, and thus can be used as a rigorous basis for many supervised and unsupervised analyses. Leveraging this novel framework, we find that the stochastic geometries of neurobiological representations of oriented visual gratings and naturalistic scenes respectively resemble untrained and trained deep network representations. Further, we are able to more accurately predict certain network attributes (e.g. training hyperparameters) from its position in stochastic (versus deterministic) shape space.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2211.11665")
get_code_for_paper("2211.11665")
have("2211.11665")
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