{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:NVILW2EVHZHBVTEXJ5FDUKLVRY","short_pith_number":"pith:NVILW2EV","canonical_record":{"source":{"id":"1812.11446","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-29T23:31:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5e70ee9df4614505b3e0694de2fd325db3b9b32f1c0c1537bf1eeb12914580fe","abstract_canon_sha256":"5044879db626eda9106451564326828c6fdd8a6165372ea711ecb30d35bfec4c"},"schema_version":"1.0"},"canonical_sha256":"6d50bb68953e4e1acc974f4a3a29758e025b41a0ed65f8cec8a4b78a7b7e8add","source":{"kind":"arxiv","id":"1812.11446","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.11446","created_at":"2026-05-17T23:47:58Z"},{"alias_kind":"arxiv_version","alias_value":"1812.11446v3","created_at":"2026-05-17T23:47:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11446","created_at":"2026-05-17T23:47:58Z"},{"alias_kind":"pith_short_12","alias_value":"NVILW2EVHZHB","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"NVILW2EVHZHBVTEX","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"NVILW2EV","created_at":"2026-05-18T12:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:NVILW2EVHZHBVTEXJ5FDUKLVRY","target":"record","payload":{"canonical_record":{"source":{"id":"1812.11446","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-29T23:31:50Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5e70ee9df4614505b3e0694de2fd325db3b9b32f1c0c1537bf1eeb12914580fe","abstract_canon_sha256":"5044879db626eda9106451564326828c6fdd8a6165372ea711ecb30d35bfec4c"},"schema_version":"1.0"},"canonical_sha256":"6d50bb68953e4e1acc974f4a3a29758e025b41a0ed65f8cec8a4b78a7b7e8add","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:58.653468Z","signature_b64":"tPREdCtOUdSOerRIVi6CSXZCFBm83CfVQYqCkzurSVuZ/q4bmGaanHqJ02L7hkEPfytd0NlZwlh27apFeIPFDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6d50bb68953e4e1acc974f4a3a29758e025b41a0ed65f8cec8a4b78a7b7e8add","last_reissued_at":"2026-05-17T23:47:58.653045Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:58.653045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.11446","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":"2026-05-17T23:47:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D1cYHlWIeTyLhbnScpgIVZTqz7Zi22XCULczYQ3uvFMfib+YZA9wj4qOtjRPNTCuhEnLQcbjLwd27j/tV5bBBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T23:59:35.125416Z"},"content_sha256":"79b658e674b777df864f3cec0f4b97f7ecfd49cb33b94c3d103992212c58bad5","schema_version":"1.0","event_id":"sha256:79b658e674b777df864f3cec0f4b97f7ecfd49cb33b94c3d103992212c58bad5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:NVILW2EVHZHBVTEXJ5FDUKLVRY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Greedy Layerwise Learning Can Scale to ImageNet","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Edouard Oyallon, Eugene Belilovsky, Michael Eickenberg","submitted_at":"2018-12-29T23:31:50Z","abstract_excerpt":"Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11446","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":"2026-05-17T23:47:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xd+I4rt3n/tiCd/U+TKnDoQNzx346MA89ljcTBJS59p8CJ4uM82l0fswP9DTYS3hrNgEy+7lsHfGcjgoKJKRDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-22T23:59:35.125943Z"},"content_sha256":"4e9293c85edac8d6b846604953485d1dc1bebaeb28ff3b41c85bc66e9761dfa6","schema_version":"1.0","event_id":"sha256:4e9293c85edac8d6b846604953485d1dc1bebaeb28ff3b41c85bc66e9761dfa6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY/bundle.json","state_url":"https://pith.science/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY/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":"2026-06-22T23:59:35Z","links":{"resolver":"https://pith.science/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY","bundle":"https://pith.science/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY/bundle.json","state":"https://pith.science/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NVILW2EVHZHBVTEXJ5FDUKLVRY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:NVILW2EVHZHBVTEXJ5FDUKLVRY","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":"5044879db626eda9106451564326828c6fdd8a6165372ea711ecb30d35bfec4c","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-29T23:31:50Z","title_canon_sha256":"5e70ee9df4614505b3e0694de2fd325db3b9b32f1c0c1537bf1eeb12914580fe"},"schema_version":"1.0","source":{"id":"1812.11446","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.11446","created_at":"2026-05-17T23:47:58Z"},{"alias_kind":"arxiv_version","alias_value":"1812.11446v3","created_at":"2026-05-17T23:47:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11446","created_at":"2026-05-17T23:47:58Z"},{"alias_kind":"pith_short_12","alias_value":"NVILW2EVHZHB","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"NVILW2EVHZHBVTEX","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"NVILW2EV","created_at":"2026-05-18T12:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:4e9293c85edac8d6b846604953485d1dc1bebaeb28ff3b41c85bc66e9761dfa6","target":"graph","created_at":"2026-05-17T23:47:58Z","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":"Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-","authors_text":"Edouard Oyallon, Eugene Belilovsky, Michael Eickenberg","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-29T23:31:50Z","title":"Greedy Layerwise Learning Can Scale to ImageNet"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11446","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":"sha256:79b658e674b777df864f3cec0f4b97f7ecfd49cb33b94c3d103992212c58bad5","target":"record","created_at":"2026-05-17T23:47:58Z","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":"5044879db626eda9106451564326828c6fdd8a6165372ea711ecb30d35bfec4c","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.LG","submitted_at":"2018-12-29T23:31:50Z","title_canon_sha256":"5e70ee9df4614505b3e0694de2fd325db3b9b32f1c0c1537bf1eeb12914580fe"},"schema_version":"1.0","source":{"id":"1812.11446","kind":"arxiv","version":3}},"canonical_sha256":"6d50bb68953e4e1acc974f4a3a29758e025b41a0ed65f8cec8a4b78a7b7e8add","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6d50bb68953e4e1acc974f4a3a29758e025b41a0ed65f8cec8a4b78a7b7e8add","first_computed_at":"2026-05-17T23:47:58.653045Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:47:58.653045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tPREdCtOUdSOerRIVi6CSXZCFBm83CfVQYqCkzurSVuZ/q4bmGaanHqJ02L7hkEPfytd0NlZwlh27apFeIPFDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:47:58.653468Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.11446","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:79b658e674b777df864f3cec0f4b97f7ecfd49cb33b94c3d103992212c58bad5","sha256:4e9293c85edac8d6b846604953485d1dc1bebaeb28ff3b41c85bc66e9761dfa6"],"state_sha256":"cf203b4a5ad67344c52360a3db28c5da54313f6ece6c9abfd60106efe831fd60"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4zWDqFMaMr0JB+1rqz9+Jei5qO5nd3u42/7HGEi4jebbgm1SZmUGxm2aRBi2rKifJfS9Nr/aHv0zilc3KW3uCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-22T23:59:35.128892Z","bundle_sha256":"120540798db9ee14f1de38b80d538212bfb442989f8e71b748353bd1e54311ec"}}