{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:2GP4QQWK75QNWT456DX5XAEV4V","short_pith_number":"pith:2GP4QQWK","canonical_record":{"source":{"id":"1808.03570","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-10T14:54:10Z","cross_cats_sorted":[],"title_canon_sha256":"14570640c5d8b6099c3c62230d4492fc344e2cc11041e3eeb380835c7505512e","abstract_canon_sha256":"a3fd7fe1a11077164918baf63e59c6f5a4bb3f62df6ad78e9dc7c1b64ff1dde5"},"schema_version":"1.0"},"canonical_sha256":"d19fc842caff60db4f9df0efdb8095e56e45457f431f4ec407989e6ca72f740c","source":{"kind":"arxiv","id":"1808.03570","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.03570","created_at":"2026-05-18T00:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"1808.03570v1","created_at":"2026-05-18T00:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.03570","created_at":"2026-05-18T00:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"2GP4QQWK75QN","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2GP4QQWK75QNWT45","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2GP4QQWK","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:2GP4QQWK75QNWT456DX5XAEV4V","target":"record","payload":{"canonical_record":{"source":{"id":"1808.03570","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-10T14:54:10Z","cross_cats_sorted":[],"title_canon_sha256":"14570640c5d8b6099c3c62230d4492fc344e2cc11041e3eeb380835c7505512e","abstract_canon_sha256":"a3fd7fe1a11077164918baf63e59c6f5a4bb3f62df6ad78e9dc7c1b64ff1dde5"},"schema_version":"1.0"},"canonical_sha256":"d19fc842caff60db4f9df0efdb8095e56e45457f431f4ec407989e6ca72f740c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:08:24.633930Z","signature_b64":"3EeTqcMPneo6+yybPEAhAxEv5FuqZAYcbeb3uVJeW2vWweyqPqMdDZerN/w1jLj2bfUV405QW3bjghxryeEVCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d19fc842caff60db4f9df0efdb8095e56e45457f431f4ec407989e6ca72f740c","last_reissued_at":"2026-05-18T00:08:24.633441Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:08:24.633441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.03570","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":"2026-05-18T00:08:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"papbmsXu7dNhH+vNEc39G6ky2dkFmhkYbLepswSWJpUsCrHOkDbIfRDMOErn/PQ5BhfNbIYNi9RqGoqC62Z7Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T00:21:43.834303Z"},"content_sha256":"091a5b9dbb06ee004eaaa129fe6ea943b0435bbf4be67375f09b2fe14a705c2f","schema_version":"1.0","event_id":"sha256:091a5b9dbb06ee004eaaa129fe6ea943b0435bbf4be67375f09b2fe14a705c2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:2GP4QQWK75QNWT456DX5XAEV4V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Densely Connected Convolutional Networks for Speech Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chia Yu Li, Ngoc Thang Vu","submitted_at":"2018-08-10T14:54:10Z","abstract_excerpt":"This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition. DenseN-ets are very deep, compact convolutional neural networks, which have demonstrated incredible improvements over the state-of-the-art results on several data sets in computer vision. Our experimental results show that DenseNet can be used for AM significantly outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore, results on Wall Street Journal revealed that with only a half of the training data DenseNet wa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.03570","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":"2026-05-18T00:08:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"80qirxFQY96sEMZPWjVmYKkmUQ1ZSx6KWABmTeMaV/NqMirxcUtmtg8NizBfRPbRhR9Ut0zxc9MkZuLeyKLyDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T00:21:43.834657Z"},"content_sha256":"b7bfd5c6887e97ace12fb3ca8b2b17d12f786d6c52071f0cab038cfdf654de28","schema_version":"1.0","event_id":"sha256:b7bfd5c6887e97ace12fb3ca8b2b17d12f786d6c52071f0cab038cfdf654de28"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2GP4QQWK75QNWT456DX5XAEV4V/bundle.json","state_url":"https://pith.science/pith/2GP4QQWK75QNWT456DX5XAEV4V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2GP4QQWK75QNWT456DX5XAEV4V/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":"2026-06-02T00:21:43Z","links":{"resolver":"https://pith.science/pith/2GP4QQWK75QNWT456DX5XAEV4V","bundle":"https://pith.science/pith/2GP4QQWK75QNWT456DX5XAEV4V/bundle.json","state":"https://pith.science/pith/2GP4QQWK75QNWT456DX5XAEV4V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2GP4QQWK75QNWT456DX5XAEV4V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:2GP4QQWK75QNWT456DX5XAEV4V","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":"a3fd7fe1a11077164918baf63e59c6f5a4bb3f62df6ad78e9dc7c1b64ff1dde5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-10T14:54:10Z","title_canon_sha256":"14570640c5d8b6099c3c62230d4492fc344e2cc11041e3eeb380835c7505512e"},"schema_version":"1.0","source":{"id":"1808.03570","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.03570","created_at":"2026-05-18T00:08:24Z"},{"alias_kind":"arxiv_version","alias_value":"1808.03570v1","created_at":"2026-05-18T00:08:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.03570","created_at":"2026-05-18T00:08:24Z"},{"alias_kind":"pith_short_12","alias_value":"2GP4QQWK75QN","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"2GP4QQWK75QNWT45","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"2GP4QQWK","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:b7bfd5c6887e97ace12fb3ca8b2b17d12f786d6c52071f0cab038cfdf654de28","target":"graph","created_at":"2026-05-18T00:08:24Z","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":"This paper presents our latest investigation on Densely Connected Convolutional Networks (DenseNets) for acoustic modelling (AM) in automatic speech recognition. DenseN-ets are very deep, compact convolutional neural networks, which have demonstrated incredible improvements over the state-of-the-art results on several data sets in computer vision. Our experimental results show that DenseNet can be used for AM significantly outperforming other neural-based models such as DNNs, CNNs, VGGs. Furthermore, results on Wall Street Journal revealed that with only a half of the training data DenseNet wa","authors_text":"Chia Yu Li, Ngoc Thang Vu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-10T14:54:10Z","title":"Densely Connected Convolutional Networks for Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.03570","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":"sha256:091a5b9dbb06ee004eaaa129fe6ea943b0435bbf4be67375f09b2fe14a705c2f","target":"record","created_at":"2026-05-18T00:08:24Z","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":"a3fd7fe1a11077164918baf63e59c6f5a4bb3f62df6ad78e9dc7c1b64ff1dde5","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2018-08-10T14:54:10Z","title_canon_sha256":"14570640c5d8b6099c3c62230d4492fc344e2cc11041e3eeb380835c7505512e"},"schema_version":"1.0","source":{"id":"1808.03570","kind":"arxiv","version":1}},"canonical_sha256":"d19fc842caff60db4f9df0efdb8095e56e45457f431f4ec407989e6ca72f740c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d19fc842caff60db4f9df0efdb8095e56e45457f431f4ec407989e6ca72f740c","first_computed_at":"2026-05-18T00:08:24.633441Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:08:24.633441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3EeTqcMPneo6+yybPEAhAxEv5FuqZAYcbeb3uVJeW2vWweyqPqMdDZerN/w1jLj2bfUV405QW3bjghxryeEVCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:08:24.633930Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.03570","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:091a5b9dbb06ee004eaaa129fe6ea943b0435bbf4be67375f09b2fe14a705c2f","sha256:b7bfd5c6887e97ace12fb3ca8b2b17d12f786d6c52071f0cab038cfdf654de28"],"state_sha256":"55e6ab19405d83f37dc1ce3e1c69ba79c5eb74858211317bf292c19537e1dfb0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z2sVpy0MLRoNRCMWFwJz+ZwC/TnD8ZXgsTxyUkG8p3maL34y7pCFK5wzYho2DtJ8JLNuQW40hhBxncPNh93eBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T00:21:43.836528Z","bundle_sha256":"53922bfef8bb1874d5f911c9a52c04826d11c7c571f2e70e3678eb7baae033eb"}}