Joshua Vogelstein, Qingyang Wang, Michael Powell, Eric Bridgeford, Ali Geisa
We lifted 13 functions out of this paper's own repositories and ran 12 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 |
|---|---|---|
| aliceqingyangwang/weightpolarityexpr | canonical | 12 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| build_string_from_dict | Ran | aliceqingyangwang/weightpolarityexpr/CV/helperfun.py code served (permissive licence) · get_code("8850801905ebce00") |
| getSingleLayerNet | Ran | aliceqingyangwang/weightpolarityexpr/XOR/getSingleLayerNet.py code served (permissive licence) · get_code("e9dafb67fa011eea") |
| get_config | Ran | aliceqingyangwang/weightpolarityexpr/CV/batch_by_run.py code served (permissive licence) · get_code("1ed2f018d790cd11") |
| get_config | Ran | aliceqingyangwang/weightpolarityexpr/XOR/batch_by_run.py code served (permissive licence) · get_code("23ec93e259d03ad1") |
| get_dict_by_key | Ran | aliceqingyangwang/weightpolarityexpr/CV/helperfun.py code served (permissive licence) · get_code("a71e24e23ab4b237") |
| get_train_param | Ran | aliceqingyangwang/weightpolarityexpr/CV/tf_training.py code served (permissive licence) · get_code("ba8083f19762e1ab") |
| plot_diff_plus_mannwhitneyu | Ran | aliceqingyangwang/weightpolarityexpr/CV/plots.py code served (permissive licence) · get_code("90b725ddfaeb0813") |
| plot_median_plus_example | Ran | aliceqingyangwang/weightpolarityexpr/CV/plots.py code served (permissive licence) · get_code("f2aa94917445b090") |
| plot_sem | Ran | aliceqingyangwang/weightpolarityexpr/CV/plots.py code served (permissive licence) · get_code("9b95d7f67d7b9d13") |
| plot_weights | Ran | aliceqingyangwang/weightpolarityexpr/CV/helperfun.py code served (permissive licence) · get_code("f8b5c23e253c996e") |
| train_model | Ran | aliceqingyangwang/weightpolarityexpr/CV/tf_training.py code served (permissive licence) · get_code("d0012529718bb0d0") |
| update_job_list | Ran | aliceqingyangwang/weightpolarityexpr/CV/scheduler.py code served (permissive licence) · get_code("3ffd16006acd8441") |
| define_model | Not yet run | aliceqingyangwang/weightpolarityexpr/CV/tf_training.py code served (permissive licence) · get_code("e5c502cca4dcb7b4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Natural intelligences (NIs) thrive in a dynamic world -they learn quickly, sometimes with only a few samples. In contrast, artificial intelligences (AIs) typically learn with a prohibitive number of training samples and computational power. What design principle difference between NI and AI could contribute to such a discrepancy? Here, we investigate the role of weight polarity: development processes initialize NIs with advantageous polarity configurations; as NIs grow and learn, synapse magnitudes update, yet polarities are largely kept unchanged. We demonstrate with simulation and image classification tasks that if weight polarities are adequately set a priori, then networks learn with less time and data. We also explicitly illustrate situations in which a priori setting the weight polarities is disadvantageous for networks. Our work illustrates the value of weight polarities from the perspective of statistical and computational efficiency during learning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2303.17589")
get_code_for_paper("2303.17589")
have("2303.17589")
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