{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:SVXZ6ELT52GDRYXJWC4XUHRF5V","short_pith_number":"pith:SVXZ6ELT","schema_version":"1.0","canonical_sha256":"956f9f1173ee8c38e2e9b0b97a1e25ed5608e1c73823b82ebe3aa28daf157899","source":{"kind":"arxiv","id":"2207.06968","version":5},"attestation_state":"computed","paper":{"title":"DASS: Differentiable Architecture Search for Sparse neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hamid Mousavi, Masoud Daneshtalab, Mina Alibeigi, Mohammad Loni","submitted_at":"2022-07-14T14:53:50Z","abstract_excerpt":"The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current method "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2207.06968","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-07-14T14:53:50Z","cross_cats_sorted":[],"title_canon_sha256":"d0de3e162c461980039a58c198f21d07a22b6a2e1d0c48bbc7c6eda78e68ce62","abstract_canon_sha256":"6faecdcecedfaf952aaf7cc63ad41115e07f49e3d05928cf36fa40646252a40e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:09.578349Z","signature_b64":"tETesyGpXeQ7g26v8QM/wFTaxMWkPz0tvXCwTmShVsNLpM8AElnBreIqBDOA8xeyka4jmLWzdvVGxr4nREYqAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"956f9f1173ee8c38e2e9b0b97a1e25ed5608e1c73823b82ebe3aa28daf157899","last_reissued_at":"2026-07-05T11:18:09.577883Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:09.577883Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DASS: Differentiable Architecture Search for Sparse neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hamid Mousavi, Masoud Daneshtalab, Mina Alibeigi, Mohammad Loni","submitted_at":"2022-07-14T14:53:50Z","abstract_excerpt":"The deployment of Deep Neural Networks (DNNs) on edge devices is hindered by the substantial gap between performance requirements and available processing power. While recent research has made significant strides in developing pruning methods to build a sparse network for reducing the computing overhead of DNNs, there remains considerable accuracy loss, especially at high pruning ratios. We find that the architectures designed for dense networks by differentiable architecture search methods are ineffective when pruning mechanisms are applied to them. The main reason is that the current method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.06968","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2207.06968/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2207.06968","created_at":"2026-07-05T11:18:09.577940+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.06968v5","created_at":"2026-07-05T11:18:09.577940+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.06968","created_at":"2026-07-05T11:18:09.577940+00:00"},{"alias_kind":"pith_short_12","alias_value":"SVXZ6ELT52GD","created_at":"2026-07-05T11:18:09.577940+00:00"},{"alias_kind":"pith_short_16","alias_value":"SVXZ6ELT52GDRYXJ","created_at":"2026-07-05T11:18:09.577940+00:00"},{"alias_kind":"pith_short_8","alias_value":"SVXZ6ELT","created_at":"2026-07-05T11:18:09.577940+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V","json":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V.json","graph_json":"https://pith.science/api/pith-number/SVXZ6ELT52GDRYXJWC4XUHRF5V/graph.json","events_json":"https://pith.science/api/pith-number/SVXZ6ELT52GDRYXJWC4XUHRF5V/events.json","paper":"https://pith.science/paper/SVXZ6ELT"},"agent_actions":{"view_html":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V","download_json":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V.json","view_paper":"https://pith.science/paper/SVXZ6ELT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.06968&json=true","fetch_graph":"https://pith.science/api/pith-number/SVXZ6ELT52GDRYXJWC4XUHRF5V/graph.json","fetch_events":"https://pith.science/api/pith-number/SVXZ6ELT52GDRYXJWC4XUHRF5V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V/action/storage_attestation","attest_author":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V/action/author_attestation","sign_citation":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V/action/citation_signature","submit_replication":"https://pith.science/pith/SVXZ6ELT52GDRYXJWC4XUHRF5V/action/replication_record"}},"created_at":"2026-07-05T11:18:09.577940+00:00","updated_at":"2026-07-05T11:18:09.577940+00:00"}