{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:FQYXKKR7ZEI7BX67QQB5RUMOWJ","short_pith_number":"pith:FQYXKKR7","canonical_record":{"source":{"id":"1612.02575","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T09:35:13Z","cross_cats_sorted":[],"title_canon_sha256":"9e12af9d44464189d4c18175c9f10c4ce6e40dbaad7d68ebc10d1ba8fdf4af4b","abstract_canon_sha256":"601601b3f58ee4993002b7e50df48f299985dcae18d2de9e3c952f0acda29d3f"},"schema_version":"1.0"},"canonical_sha256":"2c31752a3fc911f0dfdf8403d8d18eb2542264e6049e79222950053f2b1b8c3f","source":{"kind":"arxiv","id":"1612.02575","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02575","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02575v1","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02575","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"pith_short_12","alias_value":"FQYXKKR7ZEI7","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"FQYXKKR7ZEI7BX67","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"FQYXKKR7","created_at":"2026-05-18T12:30:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:FQYXKKR7ZEI7BX67QQB5RUMOWJ","target":"record","payload":{"canonical_record":{"source":{"id":"1612.02575","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T09:35:13Z","cross_cats_sorted":[],"title_canon_sha256":"9e12af9d44464189d4c18175c9f10c4ce6e40dbaad7d68ebc10d1ba8fdf4af4b","abstract_canon_sha256":"601601b3f58ee4993002b7e50df48f299985dcae18d2de9e3c952f0acda29d3f"},"schema_version":"1.0"},"canonical_sha256":"2c31752a3fc911f0dfdf8403d8d18eb2542264e6049e79222950053f2b1b8c3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:33.337385Z","signature_b64":"1ta+NvnVz0W9+dduC5H5hl12i5YQMJO1sz6gT6HMn1ummeOl6hUI9KJs2VGGwts+r0xb0KGSRXRyCYTiyb5ZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c31752a3fc911f0dfdf8403d8d18eb2542264e6049e79222950053f2b1b8c3f","last_reissued_at":"2026-05-18T00:55:33.336717Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:33.336717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1612.02575","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":"2026-05-18T00:55:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ta4Vg3BSXXZdiGxLZ2nLnQrjUQQvLxnvR0Dpa7YgG80Y2izBeo0HGEOO0YeZ7ii6pKNNQ3ZmVgDbHCtVNJ6/CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T15:01:07.889012Z"},"content_sha256":"9579423ed250d883883d60f735b864d7eef022ef30e83fdee9e25ba31b98ee4a","schema_version":"1.0","event_id":"sha256:9579423ed250d883883d60f735b864d7eef022ef30e83fdee9e25ba31b98ee4a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:FQYXKKR7ZEI7BX67QQB5RUMOWJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Filter sharing: Efficient learning of parameters for volumetric convolutions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani, Sheshadri Thiruvenkadam, Vivek Vaidya","submitted_at":"2016-12-08T09:35:13Z","abstract_excerpt":"Typical convolutional neural networks (CNNs) have several millions of parameters and require a large amount of annotated data to train them. In medical applications where training data is hard to come by, these sophisticated machine learning models are difficult to train. In this paper, we propose a method to reduce the inherent complexity of CNNs during training by exploiting the significant redundancy that is noticed in the learnt CNN filters. Our method relies on finding a small set of filters and mixing coefficients to derive every filter in each convolutional layer at the time of training"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02575","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":"2026-05-18T00:55:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"u7iYKfqZr1uS2qPPIwTtp+XhqwvyHcPUtqQi5BnwmpUBm+rhEmM+MJ2l7nl0v34G48gtmKQQt3aKzpLrwBYUAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T15:01:07.889569Z"},"content_sha256":"f6bbc81132bf9df8fd713c06f54b2aa5bd7f1a2472bde2cdcb1acd8b9a7f418a","schema_version":"1.0","event_id":"sha256:f6bbc81132bf9df8fd713c06f54b2aa5bd7f1a2472bde2cdcb1acd8b9a7f418a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ/bundle.json","state_url":"https://pith.science/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ/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":"2026-06-09T15:01:07Z","links":{"resolver":"https://pith.science/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ","bundle":"https://pith.science/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ/bundle.json","state":"https://pith.science/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FQYXKKR7ZEI7BX67QQB5RUMOWJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:FQYXKKR7ZEI7BX67QQB5RUMOWJ","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":"601601b3f58ee4993002b7e50df48f299985dcae18d2de9e3c952f0acda29d3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T09:35:13Z","title_canon_sha256":"9e12af9d44464189d4c18175c9f10c4ce6e40dbaad7d68ebc10d1ba8fdf4af4b"},"schema_version":"1.0","source":{"id":"1612.02575","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02575","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02575v1","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02575","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"pith_short_12","alias_value":"FQYXKKR7ZEI7","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_16","alias_value":"FQYXKKR7ZEI7BX67","created_at":"2026-05-18T12:30:15Z"},{"alias_kind":"pith_short_8","alias_value":"FQYXKKR7","created_at":"2026-05-18T12:30:15Z"}],"graph_snapshots":[{"event_id":"sha256:f6bbc81132bf9df8fd713c06f54b2aa5bd7f1a2472bde2cdcb1acd8b9a7f418a","target":"graph","created_at":"2026-05-18T00:55:33Z","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":"Typical convolutional neural networks (CNNs) have several millions of parameters and require a large amount of annotated data to train them. In medical applications where training data is hard to come by, these sophisticated machine learning models are difficult to train. In this paper, we propose a method to reduce the inherent complexity of CNNs during training by exploiting the significant redundancy that is noticed in the learnt CNN filters. Our method relies on finding a small set of filters and mixing coefficients to derive every filter in each convolutional layer at the time of training","authors_text":"Hariharan Ravishankar, Prasad Sudhakar, Rahul Venkataramani, Sheshadri Thiruvenkadam, Vivek Vaidya","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T09:35:13Z","title":"Filter sharing: Efficient learning of parameters for volumetric convolutions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02575","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":"sha256:9579423ed250d883883d60f735b864d7eef022ef30e83fdee9e25ba31b98ee4a","target":"record","created_at":"2026-05-18T00:55:33Z","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":"601601b3f58ee4993002b7e50df48f299985dcae18d2de9e3c952f0acda29d3f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T09:35:13Z","title_canon_sha256":"9e12af9d44464189d4c18175c9f10c4ce6e40dbaad7d68ebc10d1ba8fdf4af4b"},"schema_version":"1.0","source":{"id":"1612.02575","kind":"arxiv","version":1}},"canonical_sha256":"2c31752a3fc911f0dfdf8403d8d18eb2542264e6049e79222950053f2b1b8c3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c31752a3fc911f0dfdf8403d8d18eb2542264e6049e79222950053f2b1b8c3f","first_computed_at":"2026-05-18T00:55:33.336717Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:55:33.336717Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1ta+NvnVz0W9+dduC5H5hl12i5YQMJO1sz6gT6HMn1ummeOl6hUI9KJs2VGGwts+r0xb0KGSRXRyCYTiyb5ZCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:55:33.337385Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.02575","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9579423ed250d883883d60f735b864d7eef022ef30e83fdee9e25ba31b98ee4a","sha256:f6bbc81132bf9df8fd713c06f54b2aa5bd7f1a2472bde2cdcb1acd8b9a7f418a"],"state_sha256":"484652ede1a59358ce46c36171ca0f5032eb5365cf234e71f288fe24bf6d1d9c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QF9q7BQBm9iDsJMoh5CJlVH+B4K4ao1UWLaTEsZrGXTvsJ3ug2ZqG7LOFUhDXs1lkaUe9X0q9ZsxIesSYE37Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T15:01:07.892181Z","bundle_sha256":"dc7f443f2e31f188c2962e511a61ae083d2c0987502798ba022b898dff2129ce"}}