{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:J6PUQRWBTE3BNZZNK4EDTJQEJR","short_pith_number":"pith:J6PUQRWB","schema_version":"1.0","canonical_sha256":"4f9f4846c1993616e72d570839a6044c572a2c060c2204b87e3e8c58fcaeeaaa","source":{"kind":"arxiv","id":"1301.3124","version":4},"attestation_state":"computed","paper":{"title":"Deep learning and the renormalization group","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"C\\'edric B\\'eny","submitted_at":"2013-01-14T20:50:08Z","abstract_excerpt":"Renormalization group (RG) methods, which model the way in which the effective behavior of a system depends on the scale at which it is observed, are key to modern condensed-matter theory and particle physics. We compare the ideas behind the RG on the one hand and deep machine learning on the other, where depth and scale play a similar role. In order to illustrate this connection, we review a recent numerical method based on the RG---the multiscale entanglement renormalization ansatz (MERA)---and show how it can be converted into a learning algorithm based on a generative hierarchical Bayesian"},"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":"1301.3124","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"quant-ph","submitted_at":"2013-01-14T20:50:08Z","cross_cats_sorted":[],"title_canon_sha256":"e3bc49d185e467f8ebdc3b56377f9390b19193346daf437588cd20edafb5964b","abstract_canon_sha256":"f701b7b926ae1a388d6167a3ee004f55e16eb26991c4646663f7191a99a29d2c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:31:06.282791Z","signature_b64":"o7WE3Tj8KihsB+N5kv3+cdSTo4mf1pfbhCD3cwxp6ltwFwR1vW0Bms77E5iEu+93yZzxrslAe55LjlJZUnQNBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f9f4846c1993616e72d570839a6044c572a2c060c2204b87e3e8c58fcaeeaaa","last_reissued_at":"2026-05-18T03:31:06.282037Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:31:06.282037Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning and the renormalization group","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"C\\'edric B\\'eny","submitted_at":"2013-01-14T20:50:08Z","abstract_excerpt":"Renormalization group (RG) methods, which model the way in which the effective behavior of a system depends on the scale at which it is observed, are key to modern condensed-matter theory and particle physics. We compare the ideas behind the RG on the one hand and deep machine learning on the other, where depth and scale play a similar role. In order to illustrate this connection, we review a recent numerical method based on the RG---the multiscale entanglement renormalization ansatz (MERA)---and show how it can be converted into a learning algorithm based on a generative hierarchical Bayesian"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1301.3124","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1301.3124","created_at":"2026-05-18T03:31:06.282161+00:00"},{"alias_kind":"arxiv_version","alias_value":"1301.3124v4","created_at":"2026-05-18T03:31:06.282161+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1301.3124","created_at":"2026-05-18T03:31:06.282161+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6PUQRWBTE3B","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6PUQRWBTE3BNZZN","created_at":"2026-05-18T12:27:49.015174+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6PUQRWB","created_at":"2026-05-18T12:27:49.015174+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.04016","citing_title":"Dreaming up scale invariance via inverse renormalization group","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2605.05493","citing_title":"A renormalization-group inspired lattice-based framework for piecewise generalized linear models","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR","json":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR.json","graph_json":"https://pith.science/api/pith-number/J6PUQRWBTE3BNZZNK4EDTJQEJR/graph.json","events_json":"https://pith.science/api/pith-number/J6PUQRWBTE3BNZZNK4EDTJQEJR/events.json","paper":"https://pith.science/paper/J6PUQRWB"},"agent_actions":{"view_html":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR","download_json":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR.json","view_paper":"https://pith.science/paper/J6PUQRWB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1301.3124&json=true","fetch_graph":"https://pith.science/api/pith-number/J6PUQRWBTE3BNZZNK4EDTJQEJR/graph.json","fetch_events":"https://pith.science/api/pith-number/J6PUQRWBTE3BNZZNK4EDTJQEJR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR/action/storage_attestation","attest_author":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR/action/author_attestation","sign_citation":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR/action/citation_signature","submit_replication":"https://pith.science/pith/J6PUQRWBTE3BNZZNK4EDTJQEJR/action/replication_record"}},"created_at":"2026-05-18T03:31:06.282161+00:00","updated_at":"2026-05-18T03:31:06.282161+00:00"}