{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:TU4FZVTVA44X3WZIRFDXMM3GME","short_pith_number":"pith:TU4FZVTV","canonical_record":{"source":{"id":"1907.02051","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-03T17:37:35Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"c04d0a84c278225d9d7d648353e635ac4b84013e6d8155dbf1989278684d1816","abstract_canon_sha256":"85a5bd95d5166976e763e8df31417ea464fb66db8818cb58ec7c6026d78b8f99"},"schema_version":"1.0"},"canonical_sha256":"9d385cd67507397ddb288947763366610be49f78f96193c7774e767b8156f551","source":{"kind":"arxiv","id":"1907.02051","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.02051","created_at":"2026-05-17T23:41:31Z"},{"alias_kind":"arxiv_version","alias_value":"1907.02051v1","created_at":"2026-05-17T23:41:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.02051","created_at":"2026-05-17T23:41:31Z"},{"alias_kind":"pith_short_12","alias_value":"TU4FZVTVA44X","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"TU4FZVTVA44X3WZI","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"TU4FZVTV","created_at":"2026-05-18T12:33:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:TU4FZVTVA44X3WZIRFDXMM3GME","target":"record","payload":{"canonical_record":{"source":{"id":"1907.02051","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-03T17:37:35Z","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"title_canon_sha256":"c04d0a84c278225d9d7d648353e635ac4b84013e6d8155dbf1989278684d1816","abstract_canon_sha256":"85a5bd95d5166976e763e8df31417ea464fb66db8818cb58ec7c6026d78b8f99"},"schema_version":"1.0"},"canonical_sha256":"9d385cd67507397ddb288947763366610be49f78f96193c7774e767b8156f551","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:31.753129Z","signature_b64":"/NVDZSGlqja2aBq5uuilyLt2qpuRokT9Ec+ugZmKRjlU/vW3rCL0dY7LF5fdxhsp0SB/sGFX2HJrTOH/xuMYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d385cd67507397ddb288947763366610be49f78f96193c7774e767b8156f551","last_reissued_at":"2026-05-17T23:41:31.752383Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:31.752383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.02051","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":"2026-05-17T23:41:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eCMa2KdbaElgzObgYWRvqazLOjdEIgCI7iM5YDDdjWT55dhhaoLvbhtc2sBHVH17YPcnm7Ny/vLe3IRDN8FeCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:18:25.991089Z"},"content_sha256":"0122dbc7b042cc375083b2f68e0b0351d18beb8f8f0246c9c90495e65510628a","schema_version":"1.0","event_id":"sha256:0122dbc7b042cc375083b2f68e0b0351d18beb8f8f0246c9c90495e65510628a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:TU4FZVTVA44X3WZIRFDXMM3GME","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Spatially-Coupled Neural Network Architectures","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.ML"],"primary_cat":"cs.LG","authors_text":"Arman Hasanzadeh, Krishna R. Narayanan, Nagaraj T. Janakiraman, Vamsi K. Amalladinne","submitted_at":"2019-07-03T17:37:35Z","abstract_excerpt":"In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network. While most of the works in literature impose $\\ell_1$ regularization, DropOut or DropConnect techniques to induce sparsity, our scheme considers feature importance as a criterion to allocate the trainable parameters (resources) efficiently in the network. Even though sparsity is ensured, $\\ell_1$ regularization requires training on all the resources in a deep neural network. The DropOut/DropConnect techniques reduce the number of trainable parameters "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.02051","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":"2026-05-17T23:41:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0gggZjsRzyN8BjPXxgzFSU3MymvahOym9BHoOx5OhAE/L82XQPQvNognKWa4hYz5OFaQpn5b6tEONnXmsOwbBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T06:18:25.991793Z"},"content_sha256":"f1c3a24f93a4011d5051276985bca7180c16feb31d3a3ca90180976aa0278bf5","schema_version":"1.0","event_id":"sha256:f1c3a24f93a4011d5051276985bca7180c16feb31d3a3ca90180976aa0278bf5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TU4FZVTVA44X3WZIRFDXMM3GME/bundle.json","state_url":"https://pith.science/pith/TU4FZVTVA44X3WZIRFDXMM3GME/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TU4FZVTVA44X3WZIRFDXMM3GME/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":"2026-05-26T06:18:25Z","links":{"resolver":"https://pith.science/pith/TU4FZVTVA44X3WZIRFDXMM3GME","bundle":"https://pith.science/pith/TU4FZVTVA44X3WZIRFDXMM3GME/bundle.json","state":"https://pith.science/pith/TU4FZVTVA44X3WZIRFDXMM3GME/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TU4FZVTVA44X3WZIRFDXMM3GME/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:TU4FZVTVA44X3WZIRFDXMM3GME","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":"85a5bd95d5166976e763e8df31417ea464fb66db8818cb58ec7c6026d78b8f99","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-03T17:37:35Z","title_canon_sha256":"c04d0a84c278225d9d7d648353e635ac4b84013e6d8155dbf1989278684d1816"},"schema_version":"1.0","source":{"id":"1907.02051","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.02051","created_at":"2026-05-17T23:41:31Z"},{"alias_kind":"arxiv_version","alias_value":"1907.02051v1","created_at":"2026-05-17T23:41:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.02051","created_at":"2026-05-17T23:41:31Z"},{"alias_kind":"pith_short_12","alias_value":"TU4FZVTVA44X","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_16","alias_value":"TU4FZVTVA44X3WZI","created_at":"2026-05-18T12:33:30Z"},{"alias_kind":"pith_short_8","alias_value":"TU4FZVTV","created_at":"2026-05-18T12:33:30Z"}],"graph_snapshots":[{"event_id":"sha256:f1c3a24f93a4011d5051276985bca7180c16feb31d3a3ca90180976aa0278bf5","target":"graph","created_at":"2026-05-17T23:41:31Z","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":"In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network. While most of the works in literature impose $\\ell_1$ regularization, DropOut or DropConnect techniques to induce sparsity, our scheme considers feature importance as a criterion to allocate the trainable parameters (resources) efficiently in the network. Even though sparsity is ensured, $\\ell_1$ regularization requires training on all the resources in a deep neural network. The DropOut/DropConnect techniques reduce the number of trainable parameters ","authors_text":"Arman Hasanzadeh, Krishna R. Narayanan, Nagaraj T. Janakiraman, Vamsi K. Amalladinne","cross_cats":["cs.IT","math.IT","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-03T17:37:35Z","title":"Spatially-Coupled Neural Network Architectures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.02051","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":"sha256:0122dbc7b042cc375083b2f68e0b0351d18beb8f8f0246c9c90495e65510628a","target":"record","created_at":"2026-05-17T23:41:31Z","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":"85a5bd95d5166976e763e8df31417ea464fb66db8818cb58ec7c6026d78b8f99","cross_cats_sorted":["cs.IT","math.IT","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-03T17:37:35Z","title_canon_sha256":"c04d0a84c278225d9d7d648353e635ac4b84013e6d8155dbf1989278684d1816"},"schema_version":"1.0","source":{"id":"1907.02051","kind":"arxiv","version":1}},"canonical_sha256":"9d385cd67507397ddb288947763366610be49f78f96193c7774e767b8156f551","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d385cd67507397ddb288947763366610be49f78f96193c7774e767b8156f551","first_computed_at":"2026-05-17T23:41:31.752383Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:31.752383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/NVDZSGlqja2aBq5uuilyLt2qpuRokT9Ec+ugZmKRjlU/vW3rCL0dY7LF5fdxhsp0SB/sGFX2HJrTOH/xuMYAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:31.753129Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.02051","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0122dbc7b042cc375083b2f68e0b0351d18beb8f8f0246c9c90495e65510628a","sha256:f1c3a24f93a4011d5051276985bca7180c16feb31d3a3ca90180976aa0278bf5"],"state_sha256":"ade5b41eb53fb34f25bf355a9dd56742aed20ad11db42dba66d239b2f06b7005"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l6PrsKYHE/m/zWOfncM38REbhynTK6pzksl5dPEi1HKRo9HzlmaexNN9q+IVXBGX1m15KSzUdfLiskLqOF3FBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T06:18:25.995503Z","bundle_sha256":"0603f045a85a28dfe71332f919f7c1d3ce4d701a6676a5e713f9cd6b6b343580"}}