{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:LTGW5BY2HFK53XVXUK25LFNT22","short_pith_number":"pith:LTGW5BY2","canonical_record":{"source":{"id":"1906.04736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-11T08:38:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"05f6701212c0e025a0cc05ca96a639c7b41a35805562316e5263401338b5f01a","abstract_canon_sha256":"49be705bfeb2003010f898403b371e845dfaf2c5b7b9e73faaa2f7e6cae162a9"},"schema_version":"1.0"},"canonical_sha256":"5ccd6e871a3955dddeb7a2b5d595b3d6b002ba888174d446f23fd376bb73f544","source":{"kind":"arxiv","id":"1906.04736","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04736","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04736v1","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04736","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"pith_short_12","alias_value":"LTGW5BY2HFK5","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LTGW5BY2HFK53XVX","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LTGW5BY2","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:LTGW5BY2HFK53XVXUK25LFNT22","target":"record","payload":{"canonical_record":{"source":{"id":"1906.04736","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-11T08:38:34Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"05f6701212c0e025a0cc05ca96a639c7b41a35805562316e5263401338b5f01a","abstract_canon_sha256":"49be705bfeb2003010f898403b371e845dfaf2c5b7b9e73faaa2f7e6cae162a9"},"schema_version":"1.0"},"canonical_sha256":"5ccd6e871a3955dddeb7a2b5d595b3d6b002ba888174d446f23fd376bb73f544","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:32.381047Z","signature_b64":"40fOJRmej4Fg5wGxgF50f+CLj/scKE7s+C8JA0c4TTTrJvn6RZgNvtX0Acbk1C/jEAIwl2pnE9o9ykQtaPuHAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ccd6e871a3955dddeb7a2b5d595b3d6b002ba888174d446f23fd376bb73f544","last_reissued_at":"2026-05-17T23:43:32.380590Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:32.380590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.04736","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":"2026-05-17T23:43:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"76gcO6ARjbJUlpzxNWVT1JbqZAD0WUwMIXAdnJGC+gmUD8932C9Aw4C1Rsb09lOK7XMEpN2nxwJfWthTHcSCAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T08:13:51.258098Z"},"content_sha256":"9c079baa407dbdad93456cb6baa9524514aa2c70124959ca0d401c315b526c27","schema_version":"1.0","event_id":"sha256:9c079baa407dbdad93456cb6baa9524514aa2c70124959ca0d401c315b526c27"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:LTGW5BY2HFK53XVXUK25LFNT22","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Reproducible Deep Learning Workflows with DeepDIVA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Lars V\\\"ogtlin, Marcel W\\\"ursch, Marcus Liwicki, Michele Alberti, Rolf Ingold, Vinaychandran Pondenkandath","submitted_at":"2019-06-11T08:38:34Z","abstract_excerpt":"The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment man"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04736","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":"2026-05-17T23:43:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yl2LcDpfjln3ylZeFtTYgSn/9kPg7sqFxJ/S9sJpguXBoJDXqDeoVCaLWLLDEWmBFPD9Q15U+IraZhTtIrwSAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T08:13:51.258467Z"},"content_sha256":"136cbb649d8e53ad6a35fdee0197b048fc1ffce41dd2ea70ba2f791e831152f8","schema_version":"1.0","event_id":"sha256:136cbb649d8e53ad6a35fdee0197b048fc1ffce41dd2ea70ba2f791e831152f8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LTGW5BY2HFK53XVXUK25LFNT22/bundle.json","state_url":"https://pith.science/pith/LTGW5BY2HFK53XVXUK25LFNT22/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LTGW5BY2HFK53XVXUK25LFNT22/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":"2026-06-02T08:13:51Z","links":{"resolver":"https://pith.science/pith/LTGW5BY2HFK53XVXUK25LFNT22","bundle":"https://pith.science/pith/LTGW5BY2HFK53XVXUK25LFNT22/bundle.json","state":"https://pith.science/pith/LTGW5BY2HFK53XVXUK25LFNT22/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LTGW5BY2HFK53XVXUK25LFNT22/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LTGW5BY2HFK53XVXUK25LFNT22","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":"49be705bfeb2003010f898403b371e845dfaf2c5b7b9e73faaa2f7e6cae162a9","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-11T08:38:34Z","title_canon_sha256":"05f6701212c0e025a0cc05ca96a639c7b41a35805562316e5263401338b5f01a"},"schema_version":"1.0","source":{"id":"1906.04736","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.04736","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"arxiv_version","alias_value":"1906.04736v1","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04736","created_at":"2026-05-17T23:43:32Z"},{"alias_kind":"pith_short_12","alias_value":"LTGW5BY2HFK5","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LTGW5BY2HFK53XVX","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LTGW5BY2","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:136cbb649d8e53ad6a35fdee0197b048fc1ffce41dd2ea70ba2f791e831152f8","target":"graph","created_at":"2026-05-17T23:43:32Z","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":"The field of deep learning is experiencing a trend towards producing reproducible research. Nevertheless, it is still often a frustrating experience to reproduce scientific results. This is especially true in the machine learning community, where it is considered acceptable to have black boxes in your experiments. We present DeepDIVA, a framework designed to facilitate easy experimentation and their reproduction. This framework allows researchers to share their experiments with others, while providing functionality that allows for easy experimentation, such as: boilerplate code, experiment man","authors_text":"Lars V\\\"ogtlin, Marcel W\\\"ursch, Marcus Liwicki, Michele Alberti, Rolf Ingold, Vinaychandran Pondenkandath","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-11T08:38:34Z","title":"Improving Reproducible Deep Learning Workflows with DeepDIVA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04736","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":"sha256:9c079baa407dbdad93456cb6baa9524514aa2c70124959ca0d401c315b526c27","target":"record","created_at":"2026-05-17T23:43:32Z","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":"49be705bfeb2003010f898403b371e845dfaf2c5b7b9e73faaa2f7e6cae162a9","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-06-11T08:38:34Z","title_canon_sha256":"05f6701212c0e025a0cc05ca96a639c7b41a35805562316e5263401338b5f01a"},"schema_version":"1.0","source":{"id":"1906.04736","kind":"arxiv","version":1}},"canonical_sha256":"5ccd6e871a3955dddeb7a2b5d595b3d6b002ba888174d446f23fd376bb73f544","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ccd6e871a3955dddeb7a2b5d595b3d6b002ba888174d446f23fd376bb73f544","first_computed_at":"2026-05-17T23:43:32.380590Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:43:32.380590Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"40fOJRmej4Fg5wGxgF50f+CLj/scKE7s+C8JA0c4TTTrJvn6RZgNvtX0Acbk1C/jEAIwl2pnE9o9ykQtaPuHAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:43:32.381047Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.04736","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9c079baa407dbdad93456cb6baa9524514aa2c70124959ca0d401c315b526c27","sha256:136cbb649d8e53ad6a35fdee0197b048fc1ffce41dd2ea70ba2f791e831152f8"],"state_sha256":"939d07e7f5578cf3da2b33c3abe25eeb6ea5ed6a25a64c06fe5bf4941f4b36da"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MH3gpzhemdaAv++YiVGdXkqRexA5ed3ePxYtHczVh0NNUZz9Rt0REKzuDE2kDFoAxxGrsXFfjCoUA+6Fs0ZyCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T08:13:51.260424Z","bundle_sha256":"067ecef8aae87d78036840182a27be6432273d6a43dbf450a78a5dae0a4fdf4f"}}