{"url":"/sota/stochastic-optimization-on-mnist","task":{"name":"Stochastic Optimization","url":"/task/stochastic-optimization","note":null},"dataset":{"name":"MNIST","url":"/dataset/mnist"},"category":"Miscellaneous","categories":["Methodology","Miscellaneous"],"category_note":null,"description":"**Stochastic Optimization** is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.\n\n\n<span class=\"description-source\">Source: [ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables ](https://arxiv.org/abs/1506.08004)</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["NLL"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"NLL":null}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"MLP","metrics":{"NLL":"0.0541"},"uses_additional_data":false,"paper_date":"2017-05-22","paper":"/paper/training-deep-networks-without-learning-rates","paper_url":"http://arxiv.org/abs/1705.07795v3","paper_title":"Training Deep Networks without Learning Rates Through Coin Betting","code":"https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/cocob.py","n_code_links":6,"syntology":null}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":0,"rows_with_any_sample_ran":0,"distinct_papers_with_graph_line":0,"distinct_papers_with_any_sample_ran":0,"samples_over_distinct_papers":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":0,"n_unverified":0,"n_samples":0,"n_pointer_only_licence":0,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}