{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:2OSM4LB626HWKUSCZATPMU5Y5V","short_pith_number":"pith:2OSM4LB6","canonical_record":{"source":{"id":"1607.02241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-08T06:07:03Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"fa5714c55f7fdf97bff832a84350d27f4ba3bfd64ccd1fa202c7960ad129b6c9","abstract_canon_sha256":"0610ef8ddc656fea6b30509728d6b174c7690796df2a79cbb4e3e6861c1083dd"},"schema_version":"1.0"},"canonical_sha256":"d3a4ce2c3ed78f655242c826f653b8ed50be84d88a31f65a6d6be7200f0cb836","source":{"kind":"arxiv","id":"1607.02241","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.02241","created_at":"2026-05-18T01:11:20Z"},{"alias_kind":"arxiv_version","alias_value":"1607.02241v1","created_at":"2026-05-18T01:11:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.02241","created_at":"2026-05-18T01:11:20Z"},{"alias_kind":"pith_short_12","alias_value":"2OSM4LB626HW","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"2OSM4LB626HWKUSC","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"2OSM4LB6","created_at":"2026-05-18T12:29:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:2OSM4LB626HWKUSCZATPMU5Y5V","target":"record","payload":{"canonical_record":{"source":{"id":"1607.02241","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-08T06:07:03Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"fa5714c55f7fdf97bff832a84350d27f4ba3bfd64ccd1fa202c7960ad129b6c9","abstract_canon_sha256":"0610ef8ddc656fea6b30509728d6b174c7690796df2a79cbb4e3e6861c1083dd"},"schema_version":"1.0"},"canonical_sha256":"d3a4ce2c3ed78f655242c826f653b8ed50be84d88a31f65a6d6be7200f0cb836","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:11:20.889594Z","signature_b64":"jyfnmfAAIGtiSLmm/aB5EP9H3aUbDTN6DjBweWioHEtzNChS598Uwa+oA2nsFI7+mSIKsZQ6bEx+CjvjJeccBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3a4ce2c3ed78f655242c826f653b8ed50be84d88a31f65a6d6be7200f0cb836","last_reissued_at":"2026-05-18T01:11:20.889154Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:11:20.889154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1607.02241","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":"2026-05-18T01:11:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"39Xvwopo3GukjPSVd4IFugCRMnFN6xZKijblov/ZqtxrcA2se34bTr6n92Jj8yBoRiktZc4BDhEW+aclP6LuAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T22:43:14.696034Z"},"content_sha256":"e101dcf76985fb00f967d2413630162eaf85204ca593a270cd1b4d79f012fdd9","schema_version":"1.0","event_id":"sha256:e101dcf76985fb00f967d2413630162eaf85204ca593a270cd1b4d79f012fdd9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:2OSM4LB626HWKUSCZATPMU5Y5V","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Darryl D. Lin, Sachin S. Talathi","submitted_at":"2016-07-08T06:07:03Z","abstract_excerpt":"It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging. The stochastic gradient descent algorithm becomes unstable in the presence of noisy gradient updates resulting from arithmetic with limited numeric precision. One of the well-accepted solutions facilitating the training of low precision fixed point networks is stochastic rounding. However, to the best of our knowledge, the source of the instability in training neural networks with noisy gradient updates has not been well investigated. This wo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.02241","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":"2026-05-18T01:11:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w3uf/BqzqsVQ2KsYAuXMsdp/yHDr/8IDBH+RHnPdsj94LF+ReP0VAOLw7tgL9mntwNx43CITT1fcOBPUQrArBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T22:43:14.696726Z"},"content_sha256":"016dab95a8d88573a7a27c4b4e8b403dcc8a89506cdf26992c9db8111fcf7a75","schema_version":"1.0","event_id":"sha256:016dab95a8d88573a7a27c4b4e8b403dcc8a89506cdf26992c9db8111fcf7a75"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2OSM4LB626HWKUSCZATPMU5Y5V/bundle.json","state_url":"https://pith.science/pith/2OSM4LB626HWKUSCZATPMU5Y5V/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2OSM4LB626HWKUSCZATPMU5Y5V/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":"2026-05-18T22:43:14Z","links":{"resolver":"https://pith.science/pith/2OSM4LB626HWKUSCZATPMU5Y5V","bundle":"https://pith.science/pith/2OSM4LB626HWKUSCZATPMU5Y5V/bundle.json","state":"https://pith.science/pith/2OSM4LB626HWKUSCZATPMU5Y5V/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2OSM4LB626HWKUSCZATPMU5Y5V/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:2OSM4LB626HWKUSCZATPMU5Y5V","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":"0610ef8ddc656fea6b30509728d6b174c7690796df2a79cbb4e3e6861c1083dd","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-08T06:07:03Z","title_canon_sha256":"fa5714c55f7fdf97bff832a84350d27f4ba3bfd64ccd1fa202c7960ad129b6c9"},"schema_version":"1.0","source":{"id":"1607.02241","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1607.02241","created_at":"2026-05-18T01:11:20Z"},{"alias_kind":"arxiv_version","alias_value":"1607.02241v1","created_at":"2026-05-18T01:11:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1607.02241","created_at":"2026-05-18T01:11:20Z"},{"alias_kind":"pith_short_12","alias_value":"2OSM4LB626HW","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_16","alias_value":"2OSM4LB626HWKUSC","created_at":"2026-05-18T12:29:55Z"},{"alias_kind":"pith_short_8","alias_value":"2OSM4LB6","created_at":"2026-05-18T12:29:55Z"}],"graph_snapshots":[{"event_id":"sha256:016dab95a8d88573a7a27c4b4e8b403dcc8a89506cdf26992c9db8111fcf7a75","target":"graph","created_at":"2026-05-18T01:11:20Z","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":"It is known that training deep neural networks, in particular, deep convolutional networks, with aggressively reduced numerical precision is challenging. The stochastic gradient descent algorithm becomes unstable in the presence of noisy gradient updates resulting from arithmetic with limited numeric precision. One of the well-accepted solutions facilitating the training of low precision fixed point networks is stochastic rounding. However, to the best of our knowledge, the source of the instability in training neural networks with noisy gradient updates has not been well investigated. This wo","authors_text":"Darryl D. Lin, Sachin S. Talathi","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-08T06:07:03Z","title":"Overcoming Challenges in Fixed Point Training of Deep Convolutional Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1607.02241","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:e101dcf76985fb00f967d2413630162eaf85204ca593a270cd1b4d79f012fdd9","target":"record","created_at":"2026-05-18T01:11:20Z","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":"0610ef8ddc656fea6b30509728d6b174c7690796df2a79cbb4e3e6861c1083dd","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-07-08T06:07:03Z","title_canon_sha256":"fa5714c55f7fdf97bff832a84350d27f4ba3bfd64ccd1fa202c7960ad129b6c9"},"schema_version":"1.0","source":{"id":"1607.02241","kind":"arxiv","version":1}},"canonical_sha256":"d3a4ce2c3ed78f655242c826f653b8ed50be84d88a31f65a6d6be7200f0cb836","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d3a4ce2c3ed78f655242c826f653b8ed50be84d88a31f65a6d6be7200f0cb836","first_computed_at":"2026-05-18T01:11:20.889154Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:11:20.889154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jyfnmfAAIGtiSLmm/aB5EP9H3aUbDTN6DjBweWioHEtzNChS598Uwa+oA2nsFI7+mSIKsZQ6bEx+CjvjJeccBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:11:20.889594Z","signed_message":"canonical_sha256_bytes"},"source_id":"1607.02241","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e101dcf76985fb00f967d2413630162eaf85204ca593a270cd1b4d79f012fdd9","sha256:016dab95a8d88573a7a27c4b4e8b403dcc8a89506cdf26992c9db8111fcf7a75"],"state_sha256":"4abb3b9237c036e7a868946999fb34e4bd9b12329ffed105ae2508c54a617821"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hXIAuOjG8h+wSc8EqfiSvc/2+Ptr4GUGwIVCvkfTPLC/VRtZNusNClaK8NgysXo3nu+h7mFE68lMPj9lP6KuAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-18T22:43:14.698844Z","bundle_sha256":"472c096e81c7152ec1e76053861537e667d6990408e275cb1910f35d0fa40e5d"}}