{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FXNDGHENI6AVN3DOZDNDMRJ3Q5","short_pith_number":"pith:FXNDGHEN","canonical_record":{"source":{"id":"1710.07850","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-21T20:14:00Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ffe6c0e49c2e8acebc47488c7d5405c4990f8f35cec2bd28956364d19d9be82b","abstract_canon_sha256":"e68f26465c55787fcd4abf2c066ff1a4bcdec9c80f35a60510f4e4581a7eca81"},"schema_version":"1.0"},"canonical_sha256":"2dda331c8d478156ec6ec8da36453b875371916229f455614851396d8102718f","source":{"kind":"arxiv","id":"1710.07850","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.07850","created_at":"2026-05-18T00:32:19Z"},{"alias_kind":"arxiv_version","alias_value":"1710.07850v1","created_at":"2026-05-18T00:32:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07850","created_at":"2026-05-18T00:32:19Z"},{"alias_kind":"pith_short_12","alias_value":"FXNDGHENI6AV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FXNDGHENI6AVN3DO","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FXNDGHEN","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FXNDGHENI6AVN3DOZDNDMRJ3Q5","target":"record","payload":{"canonical_record":{"source":{"id":"1710.07850","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-21T20:14:00Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ffe6c0e49c2e8acebc47488c7d5405c4990f8f35cec2bd28956364d19d9be82b","abstract_canon_sha256":"e68f26465c55787fcd4abf2c066ff1a4bcdec9c80f35a60510f4e4581a7eca81"},"schema_version":"1.0"},"canonical_sha256":"2dda331c8d478156ec6ec8da36453b875371916229f455614851396d8102718f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:19.555115Z","signature_b64":"+IyKFFjVUziuHnZhpchbXxHLwawfWBQilbtKrv0LplrvlPzIus/pNKIq8tybw2Q9ETaTNZetSRysxCPb3bPsAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dda331c8d478156ec6ec8da36453b875371916229f455614851396d8102718f","last_reissued_at":"2026-05-18T00:32:19.554746Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:19.554746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.07850","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":"2026-05-18T00:32:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9rVe4Yfv/1sa4UWDcUGINkI1mFYCktdURSciSE1JifhJI8YZe36j0XvgTl+iL0vWZ8WJAzKQ7Z+HdEbeWL5zDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T04:30:58.460617Z"},"content_sha256":"b07c1e32a8c12283115a0c098edf92ef0b436a17d3568b8bf05bc0b769b806af","schema_version":"1.0","event_id":"sha256:b07c1e32a8c12283115a0c098edf92ef0b436a17d3568b8bf05bc0b769b806af"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FXNDGHENI6AVN3DOZDNDMRJ3Q5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Neural Network Approximation using Tensor Sketching","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"stat.ML","authors_text":"Hongxia Jin, Nina Narodytska, Shiva Prasad Kasiviswanathan","submitted_at":"2017-10-21T20:14:00Z","abstract_excerpt":"Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks. In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a smaller network architecture that approximates the operation of the target network? The question is, in part, motivated by the challenge of parameter reduction (compression) in modern deep neural networks, as the ever increasing storage and memory requirements of these networks pose a prob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07850","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":"2026-05-18T00:32:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lc99mOBca74y0TtETAbNIQnSfI+XO1OYXlqZHX82qaMop5Jz3KPIbcHaRR76QxxtqzG1cbgQbHSIa9G1cuYZDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T04:30:58.460977Z"},"content_sha256":"b8cfa3b69ff12630bd908bce19edcd420db407182eafe6442858743ccd47709a","schema_version":"1.0","event_id":"sha256:b8cfa3b69ff12630bd908bce19edcd420db407182eafe6442858743ccd47709a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5/bundle.json","state_url":"https://pith.science/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5/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":"2026-05-28T04:30:58Z","links":{"resolver":"https://pith.science/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5","bundle":"https://pith.science/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5/bundle.json","state":"https://pith.science/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FXNDGHENI6AVN3DOZDNDMRJ3Q5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FXNDGHENI6AVN3DOZDNDMRJ3Q5","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":"e68f26465c55787fcd4abf2c066ff1a4bcdec9c80f35a60510f4e4581a7eca81","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-21T20:14:00Z","title_canon_sha256":"ffe6c0e49c2e8acebc47488c7d5405c4990f8f35cec2bd28956364d19d9be82b"},"schema_version":"1.0","source":{"id":"1710.07850","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.07850","created_at":"2026-05-18T00:32:19Z"},{"alias_kind":"arxiv_version","alias_value":"1710.07850v1","created_at":"2026-05-18T00:32:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07850","created_at":"2026-05-18T00:32:19Z"},{"alias_kind":"pith_short_12","alias_value":"FXNDGHENI6AV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FXNDGHENI6AVN3DO","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FXNDGHEN","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:b8cfa3b69ff12630bd908bce19edcd420db407182eafe6442858743ccd47709a","target":"graph","created_at":"2026-05-18T00:32:19Z","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":"Deep neural networks are powerful learning models that achieve state-of-the-art performance on many computer vision, speech, and language processing tasks. In this paper, we study a fundamental question that arises when designing deep network architectures: Given a target network architecture can we design a smaller network architecture that approximates the operation of the target network? The question is, in part, motivated by the challenge of parameter reduction (compression) in modern deep neural networks, as the ever increasing storage and memory requirements of these networks pose a prob","authors_text":"Hongxia Jin, Nina Narodytska, Shiva Prasad Kasiviswanathan","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-21T20:14:00Z","title":"Deep Neural Network Approximation using Tensor Sketching"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07850","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:b07c1e32a8c12283115a0c098edf92ef0b436a17d3568b8bf05bc0b769b806af","target":"record","created_at":"2026-05-18T00:32:19Z","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":"e68f26465c55787fcd4abf2c066ff1a4bcdec9c80f35a60510f4e4581a7eca81","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-10-21T20:14:00Z","title_canon_sha256":"ffe6c0e49c2e8acebc47488c7d5405c4990f8f35cec2bd28956364d19d9be82b"},"schema_version":"1.0","source":{"id":"1710.07850","kind":"arxiv","version":1}},"canonical_sha256":"2dda331c8d478156ec6ec8da36453b875371916229f455614851396d8102718f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2dda331c8d478156ec6ec8da36453b875371916229f455614851396d8102718f","first_computed_at":"2026-05-18T00:32:19.554746Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:19.554746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+IyKFFjVUziuHnZhpchbXxHLwawfWBQilbtKrv0LplrvlPzIus/pNKIq8tybw2Q9ETaTNZetSRysxCPb3bPsAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:19.555115Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.07850","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b07c1e32a8c12283115a0c098edf92ef0b436a17d3568b8bf05bc0b769b806af","sha256:b8cfa3b69ff12630bd908bce19edcd420db407182eafe6442858743ccd47709a"],"state_sha256":"cbd109e6286124f390e2416a1e23df5aa0c396814d4bb5ac2f6533bec77f46fc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mFN8Uo6uI9MX3PgOMigLk7KkoR69Nbjt1jxb0av4P8AJ27//IEM8Oqg1STPDJIFAitVb+kcLjpCyDLPbDshqAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T04:30:58.463037Z","bundle_sha256":"f1904241a47f626989305d55939c25e3ee9ae4bafba73ad83680a06b049dd42c"}}