{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:AOFYVLZQSR567J7BDTNDPBXHWU","short_pith_number":"pith:AOFYVLZQ","canonical_record":{"source":{"id":"1903.12344","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-29T03:43:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"843a0913acfea320132d52caa46c742c14ecb99a75de1c24e82e8246b9b8ae59","abstract_canon_sha256":"9efdac43de470a4f49225121344aa2318ebd9c20a7f7035db68f20b4b81f580e"},"schema_version":"1.0"},"canonical_sha256":"038b8aaf30947befa7e11cda3786e7b514810fcb52128b720cc79622c4f231de","source":{"kind":"arxiv","id":"1903.12344","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.12344","created_at":"2026-05-17T23:49:38Z"},{"alias_kind":"arxiv_version","alias_value":"1903.12344v2","created_at":"2026-05-17T23:49:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.12344","created_at":"2026-05-17T23:49:38Z"},{"alias_kind":"pith_short_12","alias_value":"AOFYVLZQSR56","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AOFYVLZQSR567J7B","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AOFYVLZQ","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:AOFYVLZQSR567J7BDTNDPBXHWU","target":"record","payload":{"canonical_record":{"source":{"id":"1903.12344","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-29T03:43:36Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"843a0913acfea320132d52caa46c742c14ecb99a75de1c24e82e8246b9b8ae59","abstract_canon_sha256":"9efdac43de470a4f49225121344aa2318ebd9c20a7f7035db68f20b4b81f580e"},"schema_version":"1.0"},"canonical_sha256":"038b8aaf30947befa7e11cda3786e7b514810fcb52128b720cc79622c4f231de","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:38.277209Z","signature_b64":"JGKyCIgDKkdbwRI8TuD0bWIPOFo8IAOboJK7S1Meeg6Iiyro9UWk1vdeSO6jfHcG3DHlBnQ5ucovD3TFcwX7AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"038b8aaf30947befa7e11cda3786e7b514810fcb52128b720cc79622c4f231de","last_reissued_at":"2026-05-17T23:49:38.276601Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:38.276601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.12344","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b+lPhQFPKRacTS1KdZ1NlksvYci5AmktkE1KBPaNpZLxrHUySz85JNqqqV2J+kPHmjTejZ89usNSUtn68SxOAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:17:44.048301Z"},"content_sha256":"15987634361d1ff5cc9afd4898c30325b376404ebd85c677c6ba9420ae4b65fe","schema_version":"1.0","event_id":"sha256:15987634361d1ff5cc9afd4898c30325b376404ebd85c677c6ba9420ae4b65fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:AOFYVLZQSR567J7BDTNDPBXHWU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Good Representation via Continuous Attention","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Liang Zhao, Wei Xu","submitted_at":"2019-03-29T03:43:36Z","abstract_excerpt":"In this paper we present our scientific discovery that good representation can be learned via continuous attention during the interaction between Unsupervised Learning(UL) and Reinforcement Learning(RL) modules driven by intrinsic motivation. Specifically, we designed intrinsic rewards generated from UL modules for driving the RL agent to focus on objects for a period of time and to learn good representations of objects for later object recognition task. We evaluate our proposed algorithm in both with and without extrinsic reward settings. Experiments with end-to-end training in simulated envi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.12344","kind":"arxiv","version":2},"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":"2026-05-17T23:49:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fL2XQZ+jpDowICMvNIqLewkTSYDJQ/HX37na2B3kExCuTubRc2KGRV8l/+VYtzWYdfHPlLGR2uT0DuezJsA9Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T14:17:44.048664Z"},"content_sha256":"91b6036315c42beda15134d7146df9575fe794d51065aedafda5eef3feea9473","schema_version":"1.0","event_id":"sha256:91b6036315c42beda15134d7146df9575fe794d51065aedafda5eef3feea9473"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AOFYVLZQSR567J7BDTNDPBXHWU/bundle.json","state_url":"https://pith.science/pith/AOFYVLZQSR567J7BDTNDPBXHWU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AOFYVLZQSR567J7BDTNDPBXHWU/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":"2026-06-07T14:17:44Z","links":{"resolver":"https://pith.science/pith/AOFYVLZQSR567J7BDTNDPBXHWU","bundle":"https://pith.science/pith/AOFYVLZQSR567J7BDTNDPBXHWU/bundle.json","state":"https://pith.science/pith/AOFYVLZQSR567J7BDTNDPBXHWU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AOFYVLZQSR567J7BDTNDPBXHWU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:AOFYVLZQSR567J7BDTNDPBXHWU","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":"9efdac43de470a4f49225121344aa2318ebd9c20a7f7035db68f20b4b81f580e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-29T03:43:36Z","title_canon_sha256":"843a0913acfea320132d52caa46c742c14ecb99a75de1c24e82e8246b9b8ae59"},"schema_version":"1.0","source":{"id":"1903.12344","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.12344","created_at":"2026-05-17T23:49:38Z"},{"alias_kind":"arxiv_version","alias_value":"1903.12344v2","created_at":"2026-05-17T23:49:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.12344","created_at":"2026-05-17T23:49:38Z"},{"alias_kind":"pith_short_12","alias_value":"AOFYVLZQSR56","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"AOFYVLZQSR567J7B","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"AOFYVLZQ","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:91b6036315c42beda15134d7146df9575fe794d51065aedafda5eef3feea9473","target":"graph","created_at":"2026-05-17T23:49:38Z","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":"In this paper we present our scientific discovery that good representation can be learned via continuous attention during the interaction between Unsupervised Learning(UL) and Reinforcement Learning(RL) modules driven by intrinsic motivation. Specifically, we designed intrinsic rewards generated from UL modules for driving the RL agent to focus on objects for a period of time and to learn good representations of objects for later object recognition task. We evaluate our proposed algorithm in both with and without extrinsic reward settings. Experiments with end-to-end training in simulated envi","authors_text":"Liang Zhao, Wei Xu","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-29T03:43:36Z","title":"Learning Good Representation via Continuous Attention"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.12344","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":"sha256:15987634361d1ff5cc9afd4898c30325b376404ebd85c677c6ba9420ae4b65fe","target":"record","created_at":"2026-05-17T23:49:38Z","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":"9efdac43de470a4f49225121344aa2318ebd9c20a7f7035db68f20b4b81f580e","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-29T03:43:36Z","title_canon_sha256":"843a0913acfea320132d52caa46c742c14ecb99a75de1c24e82e8246b9b8ae59"},"schema_version":"1.0","source":{"id":"1903.12344","kind":"arxiv","version":2}},"canonical_sha256":"038b8aaf30947befa7e11cda3786e7b514810fcb52128b720cc79622c4f231de","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"038b8aaf30947befa7e11cda3786e7b514810fcb52128b720cc79622c4f231de","first_computed_at":"2026-05-17T23:49:38.276601Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:38.276601Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JGKyCIgDKkdbwRI8TuD0bWIPOFo8IAOboJK7S1Meeg6Iiyro9UWk1vdeSO6jfHcG3DHlBnQ5ucovD3TFcwX7AA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:38.277209Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.12344","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15987634361d1ff5cc9afd4898c30325b376404ebd85c677c6ba9420ae4b65fe","sha256:91b6036315c42beda15134d7146df9575fe794d51065aedafda5eef3feea9473"],"state_sha256":"91183a8a809a8ae17101c82e10885aafc7f9c11e4279ca89a88ce8b24801f5b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mbBmw+IQ8ANkpyIRXd18iWwuhodEwRZdC3nt1drHpIMOG/6lZ3I98wC4ZlHH7MNTdU4BNeIkXkehI1mLOBTJAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T14:17:44.051025Z","bundle_sha256":"2fa1026914f75f8c9e8737799deb935716ea5d83438847ebcbac8711c0c25c38"}}