{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:CE3EK6IYDQHYEUK53NK2SS7O4R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9fc4445e6af817cc7fb791412015504bc51bb388a3dcd5f92955d2ff3a3336fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:32:44Z","title_canon_sha256":"394730f4d0d582ac89a69fc0689b3776c1135c975a3fc14b6bc8ce6135858835"},"schema_version":"1.0","source":{"id":"1802.03494","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03494","created_at":"2026-07-05T08:04:09Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03494v4","created_at":"2026-07-05T08:04:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03494","created_at":"2026-07-05T08:04:09Z"},{"alias_kind":"pith_short_12","alias_value":"CE3EK6IYDQHY","created_at":"2026-07-05T08:04:09Z"},{"alias_kind":"pith_short_16","alias_value":"CE3EK6IYDQHYEUK5","created_at":"2026-07-05T08:04:09Z"},{"alias_kind":"pith_short_8","alias_value":"CE3EK6IY","created_at":"2026-07-05T08:04:09Z"}],"graph_snapshots":[{"event_id":"sha256:6962b84c95a2726514b1b7b9f621911a832117da68dfcbe7d58f92a268f520dd","target":"graph","created_at":"2026-07-05T08:04:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1802.03494/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets. Conventional model compression techniques rely on hand-crafted heuristics and rule-based policies that require domain experts to explore the large design space trading off among model size, speed, and accuracy, which is usually sub-optimal and time-consuming. In this paper, we propose AutoML for Model Compression (AMC) which leverage reinforcement learning to provide the model compression policy. This learning-based compressio","authors_text":"Hanrui Wang, Ji Lin, Li-Jia Li, Song Han, Yihui He, Zhijian Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:32:44Z","title":"AMC: AutoML for Model Compression and Acceleration on Mobile Devices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03494","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:493c1ad90272c695f162627b594c02a4ffaaed00c50b308e87a8dbcb6a3b3390","target":"record","created_at":"2026-07-05T08:04:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9fc4445e6af817cc7fb791412015504bc51bb388a3dcd5f92955d2ff3a3336fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-10T01:32:44Z","title_canon_sha256":"394730f4d0d582ac89a69fc0689b3776c1135c975a3fc14b6bc8ce6135858835"},"schema_version":"1.0","source":{"id":"1802.03494","kind":"arxiv","version":4}},"canonical_sha256":"11364579181c0f82515ddb55a94beee450f0ee123452b439d1aa42803e512fea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"11364579181c0f82515ddb55a94beee450f0ee123452b439d1aa42803e512fea","first_computed_at":"2026-07-05T08:04:09.501186Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:04:09.501186Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2NWc4hIEH/R3G15KNgszk0GGz7q125jjQowx/HY1n2rN8VHv7A2yxdalkSfexb2YRXRRsqaEgyYjfkAp/tvDCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:04:09.501709Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.03494","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:493c1ad90272c695f162627b594c02a4ffaaed00c50b308e87a8dbcb6a3b3390","sha256:6962b84c95a2726514b1b7b9f621911a832117da68dfcbe7d58f92a268f520dd"],"state_sha256":"145cc7f3fee84d8fae3a923293c558ae52e3d15d7c3ce9c86ef7124a8986b754"}