{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:JI4TLGF6ROETOBMOMLIVUEMIZX","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":"8d1c3b84a65c2bf631f594128ba12ce9de107639cc6b886ac37023e5972c439c","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-10-03T12:52:26Z","title_canon_sha256":"726e9dcbf49af2f908d01fe33f0bdfc97848f39e42a07be85348f012e04f5ffb"},"schema_version":"1.0","source":{"id":"1610.00520","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.00520","created_at":"2026-05-18T01:03:25Z"},{"alias_kind":"arxiv_version","alias_value":"1610.00520v1","created_at":"2026-05-18T01:03:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.00520","created_at":"2026-05-18T01:03:25Z"},{"alias_kind":"pith_short_12","alias_value":"JI4TLGF6ROET","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_16","alias_value":"JI4TLGF6ROETOBMO","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_8","alias_value":"JI4TLGF6","created_at":"2026-05-18T12:30:25Z"}],"graph_snapshots":[{"event_id":"sha256:45ef6947027a7fd240365cf520f4b7e1cd7cf4e5f393388a565f5dbc8f2f17d5","target":"graph","created_at":"2026-05-18T01:03:25Z","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":"We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works. As opposed to unsupervised initialisation followed by supervised fine tuning, our method takes advantage of both unlabelled and labelled data simultaneously through mini- batch stochastic gradient descent. We tested the method with varying proportions of labelled vs unlabelled observations in frame-based phoneme classification on the TIMIT database. Our experiments show that the method outperforms standard supervised ","authors_text":"Akash Kumar Dhaka, Giampiero Salvi","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-10-03T12:52:26Z","title":"Semi-supervised Learning with Sparse Autoencoders in Phone Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.00520","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":"sha256:48fe00b4ef39c4bf2fb94daf8e50827b1443a52bde1e5a3cb753a25b892df6ca","target":"record","created_at":"2026-05-18T01:03:25Z","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":"8d1c3b84a65c2bf631f594128ba12ce9de107639cc6b886ac37023e5972c439c","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-10-03T12:52:26Z","title_canon_sha256":"726e9dcbf49af2f908d01fe33f0bdfc97848f39e42a07be85348f012e04f5ffb"},"schema_version":"1.0","source":{"id":"1610.00520","kind":"arxiv","version":1}},"canonical_sha256":"4a393598be8b8937058e62d15a1188cdc8f5edd9de55efe71c0f6a71ba3e78f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4a393598be8b8937058e62d15a1188cdc8f5edd9de55efe71c0f6a71ba3e78f2","first_computed_at":"2026-05-18T01:03:25.342606Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:03:25.342606Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YLlE2yQeY8idn8wRAP81tl7Sux9iSlZWKbVo58LQmSrH56/7USgqdnki/aKZB18fsJl6q+h6V9R9N3RbVTcQAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:03:25.343391Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.00520","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:48fe00b4ef39c4bf2fb94daf8e50827b1443a52bde1e5a3cb753a25b892df6ca","sha256:45ef6947027a7fd240365cf520f4b7e1cd7cf4e5f393388a565f5dbc8f2f17d5"],"state_sha256":"401d26c39d9c54899d71448bd80d1153282eca389fa20aedd69b0032773525da"}