{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:GRYX4CAK6FLQBDKDVWRXYGTXRF","short_pith_number":"pith:GRYX4CAK","canonical_record":{"source":{"id":"1706.02714","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2017-06-08T18:01:02Z","cross_cats_sorted":["hep-ph","math.AG","stat.ML"],"title_canon_sha256":"c10c69736e49305690fae0be34c0e54d3498475e028775cde0f40840fca271af","abstract_canon_sha256":"6848d4a6b9a2b8f1f79bc34687cd329fb3d3aadc49d047742fd2116d17c65d49"},"schema_version":"1.0"},"canonical_sha256":"34717e080af157008d43ada37c1a77897692230484f87a5ff5f01b11a9cb7aad","source":{"kind":"arxiv","id":"1706.02714","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02714","created_at":"2026-05-18T00:21:27Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02714v3","created_at":"2026-05-18T00:21:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02714","created_at":"2026-05-18T00:21:27Z"},{"alias_kind":"pith_short_12","alias_value":"GRYX4CAK6FLQ","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"GRYX4CAK6FLQBDKD","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"GRYX4CAK","created_at":"2026-05-18T12:31:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:GRYX4CAK6FLQBDKDVWRXYGTXRF","target":"record","payload":{"canonical_record":{"source":{"id":"1706.02714","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2017-06-08T18:01:02Z","cross_cats_sorted":["hep-ph","math.AG","stat.ML"],"title_canon_sha256":"c10c69736e49305690fae0be34c0e54d3498475e028775cde0f40840fca271af","abstract_canon_sha256":"6848d4a6b9a2b8f1f79bc34687cd329fb3d3aadc49d047742fd2116d17c65d49"},"schema_version":"1.0"},"canonical_sha256":"34717e080af157008d43ada37c1a77897692230484f87a5ff5f01b11a9cb7aad","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:21:27.896218Z","signature_b64":"bJ6OIptqpkycRadZPjCFGY8JVX7/atwFLv+iX2Pcw/O3tqMGpVaFRSdt7EPuUmBGDZ5d+zDx6Ji72vbmNDzcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34717e080af157008d43ada37c1a77897692230484f87a5ff5f01b11a9cb7aad","last_reissued_at":"2026-05-18T00:21:27.895564Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:21:27.895564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.02714","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:21:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jqxG8JMwFixM2d9sbLgKtrSAU4ngoOUFElLQRB80FOB0kveZhrKv2hIKRWPHfKrcvFOYklkGDepVZV2b/juQBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:20:54.047710Z"},"content_sha256":"116048539092f4addfa164d0bc724b7a892c3c442eab887060470daa534db07e","schema_version":"1.0","event_id":"sha256:116048539092f4addfa164d0bc724b7a892c3c442eab887060470daa534db07e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:GRYX4CAK6FLQBDKDVWRXYGTXRF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep-Learning the Landscape","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["hep-ph","math.AG","stat.ML"],"primary_cat":"hep-th","authors_text":"Yang-Hui He","submitted_at":"2017-06-08T18:01:02Z","abstract_excerpt":"We propose a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial and algebraic geometry. As concrete examples, we establish multi-layer neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi-Yau manifolds and vector bundles, to quiver representations for gauge theories. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minute"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02714","kind":"arxiv","version":3},"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":"2026-05-18T00:21:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6Kce9r5gSJMgFxw6yzRmfosVSapIWIObTmBpZggN5srKgMEqVyKxbI7OtDXOPtzYLK1nnCw0gd5su5XDVpyADw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T12:20:54.048758Z"},"content_sha256":"77391d1435cd692d303a1d8ec8aba4f0ca6db845241332c53aa80d9d4de5996e","schema_version":"1.0","event_id":"sha256:77391d1435cd692d303a1d8ec8aba4f0ca6db845241332c53aa80d9d4de5996e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF/bundle.json","state_url":"https://pith.science/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF/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":"2026-05-26T12:20:54Z","links":{"resolver":"https://pith.science/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF","bundle":"https://pith.science/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF/bundle.json","state":"https://pith.science/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GRYX4CAK6FLQBDKDVWRXYGTXRF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:GRYX4CAK6FLQBDKDVWRXYGTXRF","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":"6848d4a6b9a2b8f1f79bc34687cd329fb3d3aadc49d047742fd2116d17c65d49","cross_cats_sorted":["hep-ph","math.AG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2017-06-08T18:01:02Z","title_canon_sha256":"c10c69736e49305690fae0be34c0e54d3498475e028775cde0f40840fca271af"},"schema_version":"1.0","source":{"id":"1706.02714","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02714","created_at":"2026-05-18T00:21:27Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02714v3","created_at":"2026-05-18T00:21:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02714","created_at":"2026-05-18T00:21:27Z"},{"alias_kind":"pith_short_12","alias_value":"GRYX4CAK6FLQ","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_16","alias_value":"GRYX4CAK6FLQBDKD","created_at":"2026-05-18T12:31:18Z"},{"alias_kind":"pith_short_8","alias_value":"GRYX4CAK","created_at":"2026-05-18T12:31:18Z"}],"graph_snapshots":[{"event_id":"sha256:77391d1435cd692d303a1d8ec8aba4f0ca6db845241332c53aa80d9d4de5996e","target":"graph","created_at":"2026-05-18T00:21:27Z","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 a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial and algebraic geometry. As concrete examples, we establish multi-layer neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi-Yau manifolds and vector bundles, to quiver representations for gauge theories. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minute","authors_text":"Yang-Hui He","cross_cats":["hep-ph","math.AG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2017-06-08T18:01:02Z","title":"Deep-Learning the Landscape"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02714","kind":"arxiv","version":3},"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":"sha256:116048539092f4addfa164d0bc724b7a892c3c442eab887060470daa534db07e","target":"record","created_at":"2026-05-18T00:21:27Z","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":"6848d4a6b9a2b8f1f79bc34687cd329fb3d3aadc49d047742fd2116d17c65d49","cross_cats_sorted":["hep-ph","math.AG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-th","submitted_at":"2017-06-08T18:01:02Z","title_canon_sha256":"c10c69736e49305690fae0be34c0e54d3498475e028775cde0f40840fca271af"},"schema_version":"1.0","source":{"id":"1706.02714","kind":"arxiv","version":3}},"canonical_sha256":"34717e080af157008d43ada37c1a77897692230484f87a5ff5f01b11a9cb7aad","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34717e080af157008d43ada37c1a77897692230484f87a5ff5f01b11a9cb7aad","first_computed_at":"2026-05-18T00:21:27.895564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:21:27.895564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bJ6OIptqpkycRadZPjCFGY8JVX7/atwFLv+iX2Pcw/O3tqMGpVaFRSdt7EPuUmBGDZ5d+zDx6Ji72vbmNDzcCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:21:27.896218Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.02714","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:116048539092f4addfa164d0bc724b7a892c3c442eab887060470daa534db07e","sha256:77391d1435cd692d303a1d8ec8aba4f0ca6db845241332c53aa80d9d4de5996e"],"state_sha256":"9389c266471d17415845547e310b7691f2a5db6d74fea12cdc61a34f1e295261"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ki/MUHd6RoXOk2b8Hy5dnyMvHfMsnlCXH+57zwLkvbOHyuEczaPl4sbeW1hl5ecvwmhPOdG9Zny2HTYRm3JaAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T12:20:54.051912Z","bundle_sha256":"3f5c9214fde9bcf9915dca065ef5b93f51f0eb0575c6548922fee40859279e0e"}}