{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:42EFN7TA7NSCS5ZPH2DMIRZACQ","short_pith_number":"pith:42EFN7TA","canonical_record":{"source":{"id":"1810.00589","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-01T09:15:42Z","cross_cats_sorted":[],"title_canon_sha256":"98f6e34e3ae900f91000907adfe91caed44a702f2edaca70fa1599f79080908a","abstract_canon_sha256":"d347848482dc9565989d5a5adf2d7da67f2e649ccc6a59a91c40e199e400724d"},"schema_version":"1.0"},"canonical_sha256":"e68856fe60fb6429772f3e86c44720143543a66e6d255bccf1baccbc2e19b8ed","source":{"kind":"arxiv","id":"1810.00589","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.00589","created_at":"2026-05-17T23:44:42Z"},{"alias_kind":"arxiv_version","alias_value":"1810.00589v4","created_at":"2026-05-17T23:44:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00589","created_at":"2026-05-17T23:44:42Z"},{"alias_kind":"pith_short_12","alias_value":"42EFN7TA7NSC","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"42EFN7TA7NSCS5ZP","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"42EFN7TA","created_at":"2026-05-18T12:32:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:42EFN7TA7NSCS5ZPH2DMIRZACQ","target":"record","payload":{"canonical_record":{"source":{"id":"1810.00589","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-01T09:15:42Z","cross_cats_sorted":[],"title_canon_sha256":"98f6e34e3ae900f91000907adfe91caed44a702f2edaca70fa1599f79080908a","abstract_canon_sha256":"d347848482dc9565989d5a5adf2d7da67f2e649ccc6a59a91c40e199e400724d"},"schema_version":"1.0"},"canonical_sha256":"e68856fe60fb6429772f3e86c44720143543a66e6d255bccf1baccbc2e19b8ed","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:42.977404Z","signature_b64":"2fbeKHhdd4gLrIaQ/9WYs+6LkRmQwNKdPPlA10sE8rI6vd3a6wRwqgf/xT2mByTQYei6CXWfaLDF8l5Eej+5CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e68856fe60fb6429772f3e86c44720143543a66e6d255bccf1baccbc2e19b8ed","last_reissued_at":"2026-05-17T23:44:42.976823Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:42.976823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.00589","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:44:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3j3juiLFwuKrX/C+wNIrrEKxNGR2A2GzYM8iUyjJQ0pD2+owUpq01YA+9GYkA3NwFWKJANrFleLm9/f5upKnCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:55:04.293891Z"},"content_sha256":"10036d04666b18e54c62886dad640af4a16de964e25aeb60d01bde98c19a1ac5","schema_version":"1.0","event_id":"sha256:10036d04666b18e54c62886dad640af4a16de964e25aeb60d01bde98c19a1ac5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:42EFN7TA7NSCS5ZPH2DMIRZACQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Elastic Neural Networks for Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Heikki Huttunen, Shuvra S. Bhattacharyya, Yi Zhou, Yue Bai","submitted_at":"2018-10-01T09:15:42Z","abstract_excerpt":"In this work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients. To address the vanishing gradient issue, we study a framework, where we insert an intermediate output branch after each layer in the computational graph and use the corresponding prediction loss for feeding the gradient to the early layers. The framework - which we name Elastic network - is tested with several well-known networks on CIFAR10 and CIFAR100 datasets, and the experimental results show that the proposed framework improves the accuracy on both shallow"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00589","kind":"arxiv","version":4},"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":"2026-05-17T23:44:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3A5ZpVk9ruRc5+jYjZ+CZa6Fio3TX42k1Y7vQs2f/4xVo4MrTCqtg8Wima5mknDQLva8+xRcieoVNiGOFbaYCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:55:04.294234Z"},"content_sha256":"0459dba45243c218fd831544e1f8a3901c03f337680d195b92848b49d5eca5a6","schema_version":"1.0","event_id":"sha256:0459dba45243c218fd831544e1f8a3901c03f337680d195b92848b49d5eca5a6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ/bundle.json","state_url":"https://pith.science/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ/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":"2026-06-02T22:55:04Z","links":{"resolver":"https://pith.science/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ","bundle":"https://pith.science/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ/bundle.json","state":"https://pith.science/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/42EFN7TA7NSCS5ZPH2DMIRZACQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:42EFN7TA7NSCS5ZPH2DMIRZACQ","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":"d347848482dc9565989d5a5adf2d7da67f2e649ccc6a59a91c40e199e400724d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-01T09:15:42Z","title_canon_sha256":"98f6e34e3ae900f91000907adfe91caed44a702f2edaca70fa1599f79080908a"},"schema_version":"1.0","source":{"id":"1810.00589","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.00589","created_at":"2026-05-17T23:44:42Z"},{"alias_kind":"arxiv_version","alias_value":"1810.00589v4","created_at":"2026-05-17T23:44:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.00589","created_at":"2026-05-17T23:44:42Z"},{"alias_kind":"pith_short_12","alias_value":"42EFN7TA7NSC","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_16","alias_value":"42EFN7TA7NSCS5ZP","created_at":"2026-05-18T12:32:05Z"},{"alias_kind":"pith_short_8","alias_value":"42EFN7TA","created_at":"2026-05-18T12:32:05Z"}],"graph_snapshots":[{"event_id":"sha256:0459dba45243c218fd831544e1f8a3901c03f337680d195b92848b49d5eca5a6","target":"graph","created_at":"2026-05-17T23:44:42Z","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 work we propose a framework for improving the performance of any deep neural network that may suffer from vanishing gradients. To address the vanishing gradient issue, we study a framework, where we insert an intermediate output branch after each layer in the computational graph and use the corresponding prediction loss for feeding the gradient to the early layers. The framework - which we name Elastic network - is tested with several well-known networks on CIFAR10 and CIFAR100 datasets, and the experimental results show that the proposed framework improves the accuracy on both shallow","authors_text":"Heikki Huttunen, Shuvra S. Bhattacharyya, Yi Zhou, Yue Bai","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-01T09:15:42Z","title":"Elastic Neural Networks for Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.00589","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":"sha256:10036d04666b18e54c62886dad640af4a16de964e25aeb60d01bde98c19a1ac5","target":"record","created_at":"2026-05-17T23:44:42Z","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":"d347848482dc9565989d5a5adf2d7da67f2e649ccc6a59a91c40e199e400724d","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-01T09:15:42Z","title_canon_sha256":"98f6e34e3ae900f91000907adfe91caed44a702f2edaca70fa1599f79080908a"},"schema_version":"1.0","source":{"id":"1810.00589","kind":"arxiv","version":4}},"canonical_sha256":"e68856fe60fb6429772f3e86c44720143543a66e6d255bccf1baccbc2e19b8ed","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e68856fe60fb6429772f3e86c44720143543a66e6d255bccf1baccbc2e19b8ed","first_computed_at":"2026-05-17T23:44:42.976823Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:42.976823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2fbeKHhdd4gLrIaQ/9WYs+6LkRmQwNKdPPlA10sE8rI6vd3a6wRwqgf/xT2mByTQYei6CXWfaLDF8l5Eej+5CQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:42.977404Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.00589","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:10036d04666b18e54c62886dad640af4a16de964e25aeb60d01bde98c19a1ac5","sha256:0459dba45243c218fd831544e1f8a3901c03f337680d195b92848b49d5eca5a6"],"state_sha256":"3a512cdf086850b9fd84284775db90260bcd10a35fb970c3bb2d5fba3213753a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"49usnS2heaC2mYe3jgrJbW/9BOORBsjY3CC0E9uaE2GV2KQAx8VJX6f1UDVxkeY+03vk4yFD3iPUgmov9FL2Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T22:55:04.296191Z","bundle_sha256":"d91d770c041a2369e9a39be569196fe27db1921090acd8d0b6dce4265636b7e5"}}