We lifted 1 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 |
|---|---|---|
| fdalvi/NeuroX | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_top_words | Ran | fdalvi/NeuroX/neurox/analysis/corpus.py code served (permissive licence) · get_code("ac599f77c7016e75") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a toolkit to facilitate the interpretation and understanding of neural network models. The toolkit provides several methods to identify salient neurons with respect to the model itself or an external task. A user can visualize selected neurons, ablate them to measure their effect on the model accuracy, and manipulate them to control the behavior of the model at the test time. Such an analysis has a potential to serve as a springboard in various research directions, such as understanding the model, better architectural choices, model distillation and controlling data biases.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1812.09359")
get_code_for_paper("1812.09359")
have("1812.09359")
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