{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:WQX4HMNIPXWR2MESZMBLLSYEAX","short_pith_number":"pith:WQX4HMNI","canonical_record":{"source":{"id":"1908.06263","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-17T08:40:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b320712af539726b2f057b0897a3f427124c8269f21d38dd7a85f7c28d6acdbf","abstract_canon_sha256":"614cd7a69a13268179dd8f878d3bd390fae51c3617f552befd8bbd175b9442b2"},"schema_version":"1.0"},"canonical_sha256":"b42fc3b1a87ded1d3092cb02b5cb0405d3690e5a8aa408a906d70e7a92df3e21","source":{"kind":"arxiv","id":"1908.06263","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06263","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06263v3","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06263","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"pith_short_12","alias_value":"WQX4HMNIPXWR","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"pith_short_16","alias_value":"WQX4HMNIPXWR2MES","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"pith_short_8","alias_value":"WQX4HMNI","created_at":"2026-07-05T00:12:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:WQX4HMNIPXWR2MESZMBLLSYEAX","target":"record","payload":{"canonical_record":{"source":{"id":"1908.06263","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-17T08:40:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b320712af539726b2f057b0897a3f427124c8269f21d38dd7a85f7c28d6acdbf","abstract_canon_sha256":"614cd7a69a13268179dd8f878d3bd390fae51c3617f552befd8bbd175b9442b2"},"schema_version":"1.0"},"canonical_sha256":"b42fc3b1a87ded1d3092cb02b5cb0405d3690e5a8aa408a906d70e7a92df3e21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:12:29.201748Z","signature_b64":"3KR1LC/3Jdp4h9VC4byP1r2xwbuQnpYTA1uY6u9C3yTZcD3bNg+FdoRIwqSoDJ69lVOUAArAOzUA89f4Wq4HCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b42fc3b1a87ded1d3092cb02b5cb0405d3690e5a8aa408a906d70e7a92df3e21","last_reissued_at":"2026-07-05T00:12:29.201360Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:12:29.201360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.06263","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:12:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5A58/F8iLym0hI3+2qcAGFuHH7rLmWMR5w4Xiq2gpd4XZs5Gm9tud/zaBu79aXmPPNo/KrPdq2eO0GaEYtvFAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:14:12.912711Z"},"content_sha256":"398cc87190ebebde1c61112afbd0c9b75a07218482bf97cce5f6be936e20cd83","schema_version":"1.0","event_id":"sha256:398cc87190ebebde1c61112afbd0c9b75a07218482bf97cce5f6be936e20cd83"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:WQX4HMNIPXWR2MESZMBLLSYEAX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Chao Gao, Jianpeng Zhang, Jinghua Qu, Lixin Ji, Yang Liu","submitted_at":"2019-08-17T08:40:18Z","abstract_excerpt":"In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e.g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function. We draw practical advice from a wide range of empirical results. Through the sensitivity analysis, we further improve the hyperparameters settings of AGCNNs. Experiments show that our proposals could achieve an average of 0.81% and 0.67% improvements on AGCNN-NLR"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06263","kind":"arxiv","version":3},"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/1908.06263/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:12:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m0ITiVDh3flr40J74fhofkY3/VK9CwuhIlErhGctrZD3L/ZHRjebC35RAeN6ISbH29qoY+TEZgGPATRNWGAVCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:14:12.913335Z"},"content_sha256":"eb322c05e800ae57f42bd72243c66d997ec3699a148d2d75e534324bddcd54b8","schema_version":"1.0","event_id":"sha256:eb322c05e800ae57f42bd72243c66d997ec3699a148d2d75e534324bddcd54b8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WQX4HMNIPXWR2MESZMBLLSYEAX/bundle.json","state_url":"https://pith.science/pith/WQX4HMNIPXWR2MESZMBLLSYEAX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WQX4HMNIPXWR2MESZMBLLSYEAX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T14:14:12Z","links":{"resolver":"https://pith.science/pith/WQX4HMNIPXWR2MESZMBLLSYEAX","bundle":"https://pith.science/pith/WQX4HMNIPXWR2MESZMBLLSYEAX/bundle.json","state":"https://pith.science/pith/WQX4HMNIPXWR2MESZMBLLSYEAX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WQX4HMNIPXWR2MESZMBLLSYEAX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WQX4HMNIPXWR2MESZMBLLSYEAX","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":"614cd7a69a13268179dd8f878d3bd390fae51c3617f552befd8bbd175b9442b2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-17T08:40:18Z","title_canon_sha256":"b320712af539726b2f057b0897a3f427124c8269f21d38dd7a85f7c28d6acdbf"},"schema_version":"1.0","source":{"id":"1908.06263","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06263","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06263v3","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06263","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"pith_short_12","alias_value":"WQX4HMNIPXWR","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"pith_short_16","alias_value":"WQX4HMNIPXWR2MES","created_at":"2026-07-05T00:12:29Z"},{"alias_kind":"pith_short_8","alias_value":"WQX4HMNI","created_at":"2026-07-05T00:12:29Z"}],"graph_snapshots":[{"event_id":"sha256:eb322c05e800ae57f42bd72243c66d997ec3699a148d2d75e534324bddcd54b8","target":"graph","created_at":"2026-07-05T00:12:29Z","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/1908.06263/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e.g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function. We draw practical advice from a wide range of empirical results. Through the sensitivity analysis, we further improve the hyperparameters settings of AGCNNs. Experiments show that our proposals could achieve an average of 0.81% and 0.67% improvements on AGCNN-NLR","authors_text":"Chao Gao, Jianpeng Zhang, Jinghua Qu, Lixin Ji, Yang Liu","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-17T08:40:18Z","title":"A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06263","kind":"arxiv","version":3},"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":"sha256:398cc87190ebebde1c61112afbd0c9b75a07218482bf97cce5f6be936e20cd83","target":"record","created_at":"2026-07-05T00:12:29Z","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":"614cd7a69a13268179dd8f878d3bd390fae51c3617f552befd8bbd175b9442b2","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-08-17T08:40:18Z","title_canon_sha256":"b320712af539726b2f057b0897a3f427124c8269f21d38dd7a85f7c28d6acdbf"},"schema_version":"1.0","source":{"id":"1908.06263","kind":"arxiv","version":3}},"canonical_sha256":"b42fc3b1a87ded1d3092cb02b5cb0405d3690e5a8aa408a906d70e7a92df3e21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b42fc3b1a87ded1d3092cb02b5cb0405d3690e5a8aa408a906d70e7a92df3e21","first_computed_at":"2026-07-05T00:12:29.201360Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:12:29.201360Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3KR1LC/3Jdp4h9VC4byP1r2xwbuQnpYTA1uY6u9C3yTZcD3bNg+FdoRIwqSoDJ69lVOUAArAOzUA89f4Wq4HCA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:12:29.201748Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.06263","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:398cc87190ebebde1c61112afbd0c9b75a07218482bf97cce5f6be936e20cd83","sha256:eb322c05e800ae57f42bd72243c66d997ec3699a148d2d75e534324bddcd54b8"],"state_sha256":"c87f87b53e7ae2edbf80835310b0ea54311a7e70bc4f694a25d7ecde620484b4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VOma9aKCeOdfh8ButvUHcjd5YHMOE3oK8Hw8igj8OBvJU1Ta7OxX67/uuE73KwTlas8CmzdTVtWRPzEYT4GlDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T14:14:12.917078Z","bundle_sha256":"846aba7751ee2ea0882416ab76671cf95482eed6a2ac984857fa39fcc0f0242b"}}