{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZXDNTJWGZT4ENFOTSYXW2EFODG","short_pith_number":"pith:ZXDNTJWG","canonical_record":{"source":{"id":"1706.05157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-16T06:42:15Z","cross_cats_sorted":[],"title_canon_sha256":"08a4789d66aa2b6a372e28a1b0d01c9d5c65fe37095e5e2469db0eeb0a1ef940","abstract_canon_sha256":"4d38ca27fe435e46017cc367c2e252823ab621c2d4b51a0f7a63b0a0d01ba6a5"},"schema_version":"1.0"},"canonical_sha256":"cdc6d9a6c6ccf84695d3962f6d10ae19b2ad2b55bcf89af2cff40df3c32d352f","source":{"kind":"arxiv","id":"1706.05157","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05157","created_at":"2026-05-18T00:42:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05157v1","created_at":"2026-05-18T00:42:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05157","created_at":"2026-05-18T00:42:14Z"},{"alias_kind":"pith_short_12","alias_value":"ZXDNTJWGZT4E","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZXDNTJWGZT4ENFOT","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZXDNTJWG","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZXDNTJWGZT4ENFOTSYXW2EFODG","target":"record","payload":{"canonical_record":{"source":{"id":"1706.05157","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-16T06:42:15Z","cross_cats_sorted":[],"title_canon_sha256":"08a4789d66aa2b6a372e28a1b0d01c9d5c65fe37095e5e2469db0eeb0a1ef940","abstract_canon_sha256":"4d38ca27fe435e46017cc367c2e252823ab621c2d4b51a0f7a63b0a0d01ba6a5"},"schema_version":"1.0"},"canonical_sha256":"cdc6d9a6c6ccf84695d3962f6d10ae19b2ad2b55bcf89af2cff40df3c32d352f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:14.567901Z","signature_b64":"yS65oWQMb2ZutrYj8EyTslqG6q0F4cT3TOinRNLxAYY7qXDdWHTkHNPq5sdkehKPiadK/4nNYM1LZxVp2+HfBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdc6d9a6c6ccf84695d3962f6d10ae19b2ad2b55bcf89af2cff40df3c32d352f","last_reissued_at":"2026-05-18T00:42:14.567374Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:14.567374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.05157","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"C3EOEe4LjtHXy0PI2TNWKqynuPuPSm9LOC3j0i8+ZVPkMi7XzC2YTcEQL3Rgo5mKUzlue7U/yowk5EqksT6vCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T02:25:08.476156Z"},"content_sha256":"87585258384fd13c1dbd96e8ccb47604efc004891ab9c8bd2e835c49d89ea385","schema_version":"1.0","event_id":"sha256:87585258384fd13c1dbd96e8ccb47604efc004891ab9c8bd2e835c49d89ea385"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZXDNTJWGZT4ENFOTSYXW2EFODG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Fully Trainable Network with RNN-based Pooling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ce Zhu, Chris Cook, Shuai Li, Wanqing Li, Yanbo Gao","submitted_at":"2017-06-16T06:42:15Z","abstract_excerpt":"Pooling is an important component in convolutional neural networks (CNNs) for aggregating features and reducing computational burden. Compared with other components such as convolutional layers and fully connected layers which are completely learned from data, the pooling component is still handcrafted such as max pooling and average pooling. This paper proposes a learnable pooling function using recurrent neural networks (RNN) so that the pooling can be fully adapted to data and other components of the network, leading to an improved performance. Such a network with learnable pooling function"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05157","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T00:42:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GppKs7Qrh21PCGVX8NSFdEZ8o/ZHFOMYoyf9wt3SFAb0p0zk4m9y04S+M0CyTyXgXUFWEEPyycZWQNSrFHinDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T02:25:08.476837Z"},"content_sha256":"e78466d56cb0aceb27dc42138e5d2b0d3d25040bf92733e35126af19f827229a","schema_version":"1.0","event_id":"sha256:e78466d56cb0aceb27dc42138e5d2b0d3d25040bf92733e35126af19f827229a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG/bundle.json","state_url":"https://pith.science/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG/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":"2026-05-30T02:25:08Z","links":{"resolver":"https://pith.science/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG","bundle":"https://pith.science/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG/bundle.json","state":"https://pith.science/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZXDNTJWGZT4ENFOTSYXW2EFODG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZXDNTJWGZT4ENFOTSYXW2EFODG","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":"4d38ca27fe435e46017cc367c2e252823ab621c2d4b51a0f7a63b0a0d01ba6a5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-16T06:42:15Z","title_canon_sha256":"08a4789d66aa2b6a372e28a1b0d01c9d5c65fe37095e5e2469db0eeb0a1ef940"},"schema_version":"1.0","source":{"id":"1706.05157","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05157","created_at":"2026-05-18T00:42:14Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05157v1","created_at":"2026-05-18T00:42:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05157","created_at":"2026-05-18T00:42:14Z"},{"alias_kind":"pith_short_12","alias_value":"ZXDNTJWGZT4E","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZXDNTJWGZT4ENFOT","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZXDNTJWG","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:e78466d56cb0aceb27dc42138e5d2b0d3d25040bf92733e35126af19f827229a","target":"graph","created_at":"2026-05-18T00:42:14Z","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":"Pooling is an important component in convolutional neural networks (CNNs) for aggregating features and reducing computational burden. Compared with other components such as convolutional layers and fully connected layers which are completely learned from data, the pooling component is still handcrafted such as max pooling and average pooling. This paper proposes a learnable pooling function using recurrent neural networks (RNN) so that the pooling can be fully adapted to data and other components of the network, leading to an improved performance. Such a network with learnable pooling function","authors_text":"Ce Zhu, Chris Cook, Shuai Li, Wanqing Li, Yanbo Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-16T06:42:15Z","title":"A Fully Trainable Network with RNN-based Pooling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05157","kind":"arxiv","version":1},"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":"sha256:87585258384fd13c1dbd96e8ccb47604efc004891ab9c8bd2e835c49d89ea385","target":"record","created_at":"2026-05-18T00:42:14Z","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":"4d38ca27fe435e46017cc367c2e252823ab621c2d4b51a0f7a63b0a0d01ba6a5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-16T06:42:15Z","title_canon_sha256":"08a4789d66aa2b6a372e28a1b0d01c9d5c65fe37095e5e2469db0eeb0a1ef940"},"schema_version":"1.0","source":{"id":"1706.05157","kind":"arxiv","version":1}},"canonical_sha256":"cdc6d9a6c6ccf84695d3962f6d10ae19b2ad2b55bcf89af2cff40df3c32d352f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cdc6d9a6c6ccf84695d3962f6d10ae19b2ad2b55bcf89af2cff40df3c32d352f","first_computed_at":"2026-05-18T00:42:14.567374Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:14.567374Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yS65oWQMb2ZutrYj8EyTslqG6q0F4cT3TOinRNLxAYY7qXDdWHTkHNPq5sdkehKPiadK/4nNYM1LZxVp2+HfBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:14.567901Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.05157","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:87585258384fd13c1dbd96e8ccb47604efc004891ab9c8bd2e835c49d89ea385","sha256:e78466d56cb0aceb27dc42138e5d2b0d3d25040bf92733e35126af19f827229a"],"state_sha256":"71b226235d9b49236c9270f677a03a976e83e21c299b5a16f1ba1294f8d8b341"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WEGKAtPkXCo5GmAQjKNcNVahRD1ilog0BBeyWcGQ+d7yTxyX5LV5o6NMllT2qkmnFBpNFJX4wgAkpU7oOZ1qAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T02:25:08.480447Z","bundle_sha256":"b204bfb8045ee8edf4aaea2b6c6755239f506c28c6a261aa8d4658aef2d07d3d"}}