{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GO4SHUAKFZR5347I3M3GYT7ZEB","short_pith_number":"pith:GO4SHUAK","schema_version":"1.0","canonical_sha256":"33b923d00a2e63ddf3e8db366c4ff9204b7a9f87faca86919f772d9e214f8192","source":{"kind":"arxiv","id":"1611.03125","version":1},"attestation_state":"computed","paper":{"title":"A Modular Theory of Feature Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Cheng Soon Ong, Daniel McNamara, Robert C. Williamson","submitted_at":"2016-11-09T22:40:15Z","abstract_excerpt":"Learning representations of data, and in particular learning features for a subsequent prediction task, has been a fruitful area of research delivering impressive empirical results in recent years. However, relatively little is understood about what makes a representation `good'. We propose the idea of a risk gap induced by representation learning for a given prediction context, which measures the difference in the risk of some learner using the learned features as compared to the original inputs. We describe a set of sufficient conditions for unsupervised representation learning to provide a "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1611.03125","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-11-09T22:40:15Z","cross_cats_sorted":[],"title_canon_sha256":"93b3e9b2a9f286215c2cfddc4a6b9370a55d9d7f57b12d97bc6b4b3f05107cf3","abstract_canon_sha256":"83bf2fc8e7219ee9501562002c9ea939d3e5e978d160d1c811abe1e858f14cbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:59:41.270699Z","signature_b64":"w3yg2qfjJGl9bWyEljKHbWe10r5rSgSXOMaouvJaKgGt2pU73xSvXlwEMRA8+R2dLncHt6JQAqex6QOv98qVCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33b923d00a2e63ddf3e8db366c4ff9204b7a9f87faca86919f772d9e214f8192","last_reissued_at":"2026-05-18T00:59:41.269908Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:59:41.269908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Modular Theory of Feature Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Cheng Soon Ong, Daniel McNamara, Robert C. Williamson","submitted_at":"2016-11-09T22:40:15Z","abstract_excerpt":"Learning representations of data, and in particular learning features for a subsequent prediction task, has been a fruitful area of research delivering impressive empirical results in recent years. However, relatively little is understood about what makes a representation `good'. We propose the idea of a risk gap induced by representation learning for a given prediction context, which measures the difference in the risk of some learner using the learned features as compared to the original inputs. We describe a set of sufficient conditions for unsupervised representation learning to provide a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.03125","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.03125","created_at":"2026-05-18T00:59:41.270044+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.03125v1","created_at":"2026-05-18T00:59:41.270044+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.03125","created_at":"2026-05-18T00:59:41.270044+00:00"},{"alias_kind":"pith_short_12","alias_value":"GO4SHUAKFZR5","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GO4SHUAKFZR5347I","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GO4SHUAK","created_at":"2026-05-18T12:30:19.053100+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB","json":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB.json","graph_json":"https://pith.science/api/pith-number/GO4SHUAKFZR5347I3M3GYT7ZEB/graph.json","events_json":"https://pith.science/api/pith-number/GO4SHUAKFZR5347I3M3GYT7ZEB/events.json","paper":"https://pith.science/paper/GO4SHUAK"},"agent_actions":{"view_html":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB","download_json":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB.json","view_paper":"https://pith.science/paper/GO4SHUAK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.03125&json=true","fetch_graph":"https://pith.science/api/pith-number/GO4SHUAKFZR5347I3M3GYT7ZEB/graph.json","fetch_events":"https://pith.science/api/pith-number/GO4SHUAKFZR5347I3M3GYT7ZEB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB/action/storage_attestation","attest_author":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB/action/author_attestation","sign_citation":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB/action/citation_signature","submit_replication":"https://pith.science/pith/GO4SHUAKFZR5347I3M3GYT7ZEB/action/replication_record"}},"created_at":"2026-05-18T00:59:41.270044+00:00","updated_at":"2026-05-18T00:59:41.270044+00:00"}