{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:SHQ7OVK4AK3JVFJF5V4XNTONUF","short_pith_number":"pith:SHQ7OVK4","canonical_record":{"source":{"id":"1706.05394","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-16T18:11:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"87407790b7ced038e18e513b55e916c8e7b5d76ed1bbb6b022b3f4ffd3003ad8","abstract_canon_sha256":"563edd76cad0d56c9371e72b39cdb563820ee23298631d088dc51bcf8349885b"},"schema_version":"1.0"},"canonical_sha256":"91e1f7555c02b69a9525ed7976cdcda176212a4d40073b3ccc61f5585d5da98d","source":{"kind":"arxiv","id":"1706.05394","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05394","created_at":"2026-05-18T00:41:06Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05394v2","created_at":"2026-05-18T00:41:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05394","created_at":"2026-05-18T00:41:06Z"},{"alias_kind":"pith_short_12","alias_value":"SHQ7OVK4AK3J","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SHQ7OVK4AK3JVFJF","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SHQ7OVK4","created_at":"2026-05-18T12:31:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:SHQ7OVK4AK3JVFJF5V4XNTONUF","target":"record","payload":{"canonical_record":{"source":{"id":"1706.05394","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-16T18:11:09Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"87407790b7ced038e18e513b55e916c8e7b5d76ed1bbb6b022b3f4ffd3003ad8","abstract_canon_sha256":"563edd76cad0d56c9371e72b39cdb563820ee23298631d088dc51bcf8349885b"},"schema_version":"1.0"},"canonical_sha256":"91e1f7555c02b69a9525ed7976cdcda176212a4d40073b3ccc61f5585d5da98d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:06.791970Z","signature_b64":"pSd9CRiQJ9tP9Ph8UAWNGdRIN0H0vTpwz/aj7++iGL1JKNbKDA+DBIO32Xqdb7AKcIIW5tatFfnw3sHuSOrLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91e1f7555c02b69a9525ed7976cdcda176212a4d40073b3ccc61f5585d5da98d","last_reissued_at":"2026-05-18T00:41:06.791371Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:06.791371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.05394","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:41:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1HBxYNQjJlaDpwiIuLESUhFz5m6YypAmDj58B+gQIn0qVpE2OsrgTAoLuqZoWXAl/z7iVLkL/8k9zXnTbkKDCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T19:34:27.940627Z"},"content_sha256":"65901b190d43e8f576fcab75a4031f04d51c51ced7c2c80ec69f4db2005103bc","schema_version":"1.0","event_id":"sha256:65901b190d43e8f576fcab75a4031f04d51c51ced7c2c80ec69f4db2005103bc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:SHQ7OVK4AK3JVFJF5V4XNTONUF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Closer Look at Memorization in Deep Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Aaron Courville, Asja Fischer, David Krueger, Devansh Arpit, Emmanuel Bengio, Maxinder S. Kanwal, Nicolas Ballas, Simon Lacoste-Julien, Stanis{\\l}aw Jastrz\\k{e}bski, Tegan Maharaj, Yoshua Bengio","submitted_at":"2017-06-16T18:11:09Z","abstract_excerpt":"We examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05394","kind":"arxiv","version":2},"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":"2026-05-18T00:41:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DuszLvPrydYMPmEHJwxw4p7OtotzSbXpfl3YBYPWLS4ZzjaAGQpNp+buw+rzjekb4oJh1T6MVxTiUwunbj7RAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T19:34:27.940990Z"},"content_sha256":"a91f2ce23d551223aab2cfe995c08f766b4b0075ab4b4c51eb0d1a3aee5d8316","schema_version":"1.0","event_id":"sha256:a91f2ce23d551223aab2cfe995c08f766b4b0075ab4b4c51eb0d1a3aee5d8316"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF/bundle.json","state_url":"https://pith.science/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF/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":"2026-06-10T19:34:27Z","links":{"resolver":"https://pith.science/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF","bundle":"https://pith.science/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF/bundle.json","state":"https://pith.science/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SHQ7OVK4AK3JVFJF5V4XNTONUF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:SHQ7OVK4AK3JVFJF5V4XNTONUF","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":"563edd76cad0d56c9371e72b39cdb563820ee23298631d088dc51bcf8349885b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-16T18:11:09Z","title_canon_sha256":"87407790b7ced038e18e513b55e916c8e7b5d76ed1bbb6b022b3f4ffd3003ad8"},"schema_version":"1.0","source":{"id":"1706.05394","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.05394","created_at":"2026-05-18T00:41:06Z"},{"alias_kind":"arxiv_version","alias_value":"1706.05394v2","created_at":"2026-05-18T00:41:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.05394","created_at":"2026-05-18T00:41:06Z"},{"alias_kind":"pith_short_12","alias_value":"SHQ7OVK4AK3J","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_16","alias_value":"SHQ7OVK4AK3JVFJF","created_at":"2026-05-18T12:31:43Z"},{"alias_kind":"pith_short_8","alias_value":"SHQ7OVK4","created_at":"2026-05-18T12:31:43Z"}],"graph_snapshots":[{"event_id":"sha256:a91f2ce23d551223aab2cfe995c08f766b4b0075ab4b4c51eb0d1a3aee5d8316","target":"graph","created_at":"2026-05-18T00:41:06Z","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 examine the role of memorization in deep learning, drawing connections to capacity, generalization, and adversarial robustness. While deep networks are capable of memorizing noise data, our results suggest that they tend to prioritize learning simple patterns first. In our experiments, we expose qualitative differences in gradient-based optimization of deep neural networks (DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned explicit regularization (e.g., dropout) we can degrade DNN training performance on noise datasets without compromising generalization on real","authors_text":"Aaron Courville, Asja Fischer, David Krueger, Devansh Arpit, Emmanuel Bengio, Maxinder S. Kanwal, Nicolas Ballas, Simon Lacoste-Julien, Stanis{\\l}aw Jastrz\\k{e}bski, Tegan Maharaj, Yoshua Bengio","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-16T18:11:09Z","title":"A Closer Look at Memorization in Deep Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.05394","kind":"arxiv","version":2},"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":"sha256:65901b190d43e8f576fcab75a4031f04d51c51ced7c2c80ec69f4db2005103bc","target":"record","created_at":"2026-05-18T00:41:06Z","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":"563edd76cad0d56c9371e72b39cdb563820ee23298631d088dc51bcf8349885b","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-06-16T18:11:09Z","title_canon_sha256":"87407790b7ced038e18e513b55e916c8e7b5d76ed1bbb6b022b3f4ffd3003ad8"},"schema_version":"1.0","source":{"id":"1706.05394","kind":"arxiv","version":2}},"canonical_sha256":"91e1f7555c02b69a9525ed7976cdcda176212a4d40073b3ccc61f5585d5da98d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"91e1f7555c02b69a9525ed7976cdcda176212a4d40073b3ccc61f5585d5da98d","first_computed_at":"2026-05-18T00:41:06.791371Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:41:06.791371Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pSd9CRiQJ9tP9Ph8UAWNGdRIN0H0vTpwz/aj7++iGL1JKNbKDA+DBIO32Xqdb7AKcIIW5tatFfnw3sHuSOrLDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:41:06.791970Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.05394","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65901b190d43e8f576fcab75a4031f04d51c51ced7c2c80ec69f4db2005103bc","sha256:a91f2ce23d551223aab2cfe995c08f766b4b0075ab4b4c51eb0d1a3aee5d8316"],"state_sha256":"0d892f22cce4387c68ca3d5da7f69cc0706bc3cae77c087a490ee0e91a3c7ac2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NerVfZtlfbeBVk62vafjCAkKI/yYaxvIiF7Ht0B2eH8X3vWeYYzLziLeljK7x+3mdoNmuQTcyKid6GiP/85jBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T19:34:27.942990Z","bundle_sha256":"8e233ba48e0febb6fba58e749a5a7939824b7471d4e2eef3158f2de65717c187"}}