{"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/zeroth-order-topological-insights-into","title":"Zeroth-Order Topological Insights into Iterative Magnitude Pruning","arxiv_id":"2206.06563","date":"2022-06-14","proceeding":null,"authors":["Aishwarya Balwani","Jakob Krzyston"],"abstract":"Modern-day neural networks are famously large, yet also highly redundant and compressible; there exist numerous pruning strategies in the deep learning literature that yield over 90% sparser sub-networks of fully-trained, dense architectures while still maintaining their original accuracies. Amongst these many methods though -- thanks to its conceptual simplicity, ease of implementation, and efficacy -- Iterative Magnitude Pruning (IMP) dominates in practice and is the de facto baseline to beat in the pruning community. However, theoretical explanations as to why a simplistic method such as IMP works at all are few and limited. In this work, we leverage the notion of persistent homology to gain insights into the workings of IMP and show that it inherently encourages retention of those weights which preserve topological information in a trained network. Subsequently, we also provide bounds on how much different networks can be pruned while perfectly preserving their zeroth order topological features, and present a modified version of IMP to do the same.","url_abs":"https://arxiv.org/abs/2206.06563v2","url_pdf":"https://arxiv.org/pdf/2206.06563v2.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":"zeroth-order-topological-insights-into","repo_url":"https://github.com/AishwaryaHB/aishwaryahb.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.06563","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.06563"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/ganguli-lab/Synaptic-Flow","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/AishwaryaHB/aishwaryahb.github.io","reach":{"status":"ok"}}],"summary":{"ran":2},"by_repo_kind":{"found_in_text":{"samples":2,"ran":2,"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":2,"samples":[{"code_sha256_prefix":"482f4c47b4e2c46e","entry":"Mag","repo":"ganguli-lab/Synaptic-Flow","repo_kind":"found_in_text","path":"Pruners/pruners.py","file_url":"https://github.com/ganguli-lab/Synaptic-Flow/blob/HEAD/Pruners/pruners.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"482f4c47b4e2c46e"}},{"code_sha256_prefix":"25100e43d36881c9","entry":"Pruner","repo":"ganguli-lab/Synaptic-Flow","repo_kind":"found_in_text","path":"Pruners/pruners.py","file_url":"https://github.com/ganguli-lab/Synaptic-Flow/blob/HEAD/Pruners/pruners.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"25100e43d36881c9"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}