Papers › Polarity is all you need to learn and transfer faster

Polarity is all you need to learn and transfer faster

29 Mar 2023arXiv:2303.17589archive 2025-07-28

Qingyang Wang, Michael A. Powell, Ali Geisa, Eric W. Bridgeford, Joshua T. Vogelstein

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.

PaperPDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2303.17589")

Code

Syntology Ran 12 of 13 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 12 ran with no contract checked.

By repository: official repository: 13 samples from 1 repository, 12 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

aliceqingyangwang/weightpolarityexpr officialmentioned in papertfApache-2.0 report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

13 samples harvested; 12 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

12ran
1unverified

Licence: 0 of the 13 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from aliceqingyangwang/weightpolarityexpr. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

build_string_from_dict aliceqingyangwang/weightpolarityexpr/CV/helperfun.py official repository ran Apache-2.0 (permissive) · 8850801905ebce00 · report
getSingleLayerNet aliceqingyangwang/weightpolarityexpr/XOR/getSingleLayerNet.py official repository ran Apache-2.0 (permissive) · e9dafb67fa011eea · report
get_config aliceqingyangwang/weightpolarityexpr/CV/batch_by_run.py official repository ran Apache-2.0 (permissive) · 1ed2f018d790cd11 · report
get_config aliceqingyangwang/weightpolarityexpr/XOR/batch_by_run.py official repository ran Apache-2.0 (permissive) · 23ec93e259d03ad1 · report
get_dict_by_key aliceqingyangwang/weightpolarityexpr/CV/helperfun.py official repository ran Apache-2.0 (permissive) · a71e24e23ab4b237 · report
get_train_param aliceqingyangwang/weightpolarityexpr/CV/tf_training.py official repository ran Apache-2.0 (permissive) · ba8083f19762e1ab · report
plot_diff_plus_mannwhitneyu aliceqingyangwang/weightpolarityexpr/CV/plots.py official repository ran Apache-2.0 (permissive) · 90b725ddfaeb0813 · report
plot_median_plus_example aliceqingyangwang/weightpolarityexpr/CV/plots.py official repository ran Apache-2.0 (permissive) · f2aa94917445b090 · report
plot_sem aliceqingyangwang/weightpolarityexpr/CV/plots.py official repository ran Apache-2.0 (permissive) · 9b95d7f67d7b9d13 · report
plot_weights aliceqingyangwang/weightpolarityexpr/CV/helperfun.py official repository ran Apache-2.0 (permissive) · f8b5c23e253c996e · report
train_model aliceqingyangwang/weightpolarityexpr/CV/tf_training.py official repository ran Apache-2.0 (permissive) · d0012529718bb0d0 · report
update_job_list aliceqingyangwang/weightpolarityexpr/CV/scheduler.py official repository ran Apache-2.0 (permissive) · 3ffd16006acd8441 · report
define_model aliceqingyangwang/weightpolarityexpr/CV/tf_training.py official repository unverified Apache-2.0 (permissive) · e5c502cca4dcb7b4 · report

Tasks

AllComputational EfficiencyImage Classificationimage-classification

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections