{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:KMH3CNGKIEJBCMJMYQU6PGFIDU","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":"990d7cfeb8cc040742ba68b3b998c7623d13a94cc109c891676b53d11c0dd775","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T06:59:46Z","title_canon_sha256":"7153fe7135e273e083d04500e8c5fd875037494b050e096a2d1953a1d96d569b"},"schema_version":"1.0","source":{"id":"1807.10458","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10458","created_at":"2026-05-17T23:58:04Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10458v2","created_at":"2026-05-17T23:58:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10458","created_at":"2026-05-17T23:58:04Z"},{"alias_kind":"pith_short_12","alias_value":"KMH3CNGKIEJB","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"KMH3CNGKIEJBCMJM","created_at":"2026-05-18T12:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"KMH3CNGK","created_at":"2026-05-18T12:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:466c8c945dcadb65476919434530113e89140364f2602f914ef61c52b195d552","target":"graph","created_at":"2026-05-17T23:58:04Z","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"},"paper":{"abstract_excerpt":"Neural network based approximate computing is a universal architecture promising to gain tremendous energy-efficiency for many error resilient applications. To guarantee the approximation quality, existing works deploy two neural networks (NNs), e.g., an approximator and a predictor. The approximator provides the approximate results, while the predictor predicts whether the input data is safe to approximate with the given quality requirement. However, it is non-trivial and time-consuming to make these two neural network coordinate---they have different optimization objectives---by training the","authors_text":"Cewu Lu, Chengwen Xu, Li Jiang, Naifeng Jing, Xiaoyao Liang, Xuyang Chen, Zhenghao Peng","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T06:59:46Z","title":"AXNet: ApproXimate computing using an end-to-end trainable neural network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10458","kind":"arxiv","version":2},"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:a8405a18e711a88a018c0252fbf739c14e7a2f03e9ca94a595f7e3c517d3f507","target":"record","created_at":"2026-05-17T23:58:04Z","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":"990d7cfeb8cc040742ba68b3b998c7623d13a94cc109c891676b53d11c0dd775","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-07-27T06:59:46Z","title_canon_sha256":"7153fe7135e273e083d04500e8c5fd875037494b050e096a2d1953a1d96d569b"},"schema_version":"1.0","source":{"id":"1807.10458","kind":"arxiv","version":2}},"canonical_sha256":"530fb134ca411211312cc429e798a81d3bad3a3df7f2667ae95df288217edde5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"530fb134ca411211312cc429e798a81d3bad3a3df7f2667ae95df288217edde5","first_computed_at":"2026-05-17T23:58:04.332490Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:58:04.332490Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"56oqWEe9Hksyom4/A1A57qABK5Tp6UTP7UdlGs16KM7P6AUwrSUlzGhjFYg5JNvEAwItP/5tQcIqx6z+3fXtCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:58:04.333037Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.10458","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a8405a18e711a88a018c0252fbf739c14e7a2f03e9ca94a595f7e3c517d3f507","sha256:466c8c945dcadb65476919434530113e89140364f2602f914ef61c52b195d552"],"state_sha256":"c9a2ac5cf10dd9e88fe8b4f57f67a314c950524f702f4df96fb5a5bdf6713089"}