{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nas-bench-x11-and-the-power-of-learning","title":"NAS-Bench-x11 and the Power of Learning Curves","arxiv_id":"2111.03602","date":"2021-11-05","proceeding":"NeurIPS 2021 12","authors":["Shen Yan","Colin White","Yash Savani","Frank Hutter"],"abstract":"While early research in neural architecture search (NAS) required extreme computational resources, the recent releases of tabular and surrogate benchmarks have greatly increased the speed and reproducibility of NAS research. However, two of the most popular benchmarks do not provide the full training information for each architecture. As a result, on these benchmarks it is not possible to run many types of multi-fidelity techniques, such as learning curve extrapolation, that require evaluating architectures at arbitrary epochs. In this work, we present a method using singular value decomposition and noise modeling to create surrogate benchmarks, NAS-Bench-111, NAS-Bench-311, and NAS-Bench-NLP11, that output the full training information for each architecture, rather than just the final validation accuracy. We demonstrate the power of using the full training information by introducing a learning curve extrapolation framework to modify single-fidelity algorithms, showing that it leads to improvements over popular single-fidelity algorithms which claimed to be state-of-the-art upon release. Our code and pretrained models are available at https://github.com/automl/nas-bench-x11.","url_abs":"https://arxiv.org/abs/2111.03602v1","url_pdf":"https://arxiv.org/pdf/2111.03602v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nas-bench-x11-and-the-power-of-learning","repo_url":"https://github.com/automl/nas-bench-x11","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"automl","task_name":"AutoML"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2111.03602","atlas_url":"https://app.syntology.ai/?focus=2111.03602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.03602"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/automl/nas-bench-x11","reach":null}],"summary":{"ran":2,"ran_draft_wrong":1,"ran_fixture":1,"unverified":2},"by_repo_kind":{"official":{"samples":6,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"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"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"36c51e35267ab6c9","entry":"ParametricModel","repo":"automl/nas-bench-x11","repo_kind":"official","path":"naslib/predictors/lce/lce.py","file_url":"https://github.com/automl/nas-bench-x11/blob/HEAD/naslib/predictors/lce/lce.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"36c51e35267ab6c9"}},{"code_sha256_prefix":"962e3193f835c354","entry":"Predictor","repo":"automl/nas-bench-x11","repo_kind":"official","path":"naslib/predictors/lce/lce.py","file_url":"https://github.com/automl/nas-bench-x11/blob/HEAD/naslib/predictors/lce/lce.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"962e3193f835c354"}},{"code_sha256_prefix":"6795115c8f12bbd6","entry":"construct_parametric_model","repo":"automl/nas-bench-x11","repo_kind":"official","path":"naslib/predictors/lce/lce.py","file_url":"https://github.com/automl/nas-bench-x11/blob/HEAD/naslib/predictors/lce/lce.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6795115c8f12bbd6"}},{"code_sha256_prefix":"1eddae36959d2bea","entry":"optimize_model_class","repo":"automl/nas-bench-x11","repo_kind":"official","path":"naslib/predictors/lce/lce.py","file_url":"https://github.com/automl/nas-bench-x11/blob/HEAD/naslib/predictors/lce/lce.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1eddae36959d2bea"}},{"code_sha256_prefix":"39838ad0aeb9ef75","entry":"LCEPredictor","repo":"automl/nas-bench-x11","repo_kind":"official","path":"naslib/predictors/lce/lce.py","file_url":"https://github.com/automl/nas-bench-x11/blob/HEAD/naslib/predictors/lce/lce.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"39838ad0aeb9ef75"}},{"code_sha256_prefix":"b6361e24f10c28f1","entry":"ParametricEnsemble","repo":"automl/nas-bench-x11","repo_kind":"official","path":"naslib/predictors/lce/lce.py","file_url":"https://github.com/automl/nas-bench-x11/blob/HEAD/naslib/predictors/lce/lce.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b6361e24f10c28f1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}