{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OUKUSR52TMGDUH3MOZJEYJ46S5","short_pith_number":"pith:OUKUSR52","canonical_record":{"source":{"id":"2307.09994","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-19T13:58:01Z","cross_cats_sorted":["cs.CV","eess.SP"],"title_canon_sha256":"c6edf3f90307cb4522aad71167993ffb41d65a3352a6984a67121e125cba67e8","abstract_canon_sha256":"2c9873d1964af5d69ffbdb87a8f80a7e9bb0efaa5ab49258eb53e3716d0fc124"},"schema_version":"1.0"},"canonical_sha256":"75154947ba9b0c3a1f6c76524c279e9770219b8ef18adbe2d3c244229320f377","source":{"kind":"arxiv","id":"2307.09994","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09994","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09994v1","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09994","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"pith_short_12","alias_value":"OUKUSR52TMGD","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"pith_short_16","alias_value":"OUKUSR52TMGDUH3M","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"pith_short_8","alias_value":"OUKUSR52","created_at":"2026-07-05T06:32:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OUKUSR52TMGDUH3MOZJEYJ46S5","target":"record","payload":{"canonical_record":{"source":{"id":"2307.09994","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-19T13:58:01Z","cross_cats_sorted":["cs.CV","eess.SP"],"title_canon_sha256":"c6edf3f90307cb4522aad71167993ffb41d65a3352a6984a67121e125cba67e8","abstract_canon_sha256":"2c9873d1964af5d69ffbdb87a8f80a7e9bb0efaa5ab49258eb53e3716d0fc124"},"schema_version":"1.0"},"canonical_sha256":"75154947ba9b0c3a1f6c76524c279e9770219b8ef18adbe2d3c244229320f377","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:32:49.035620Z","signature_b64":"JoYurlCYpyTrR/wgkK5hmX9T454wl3c3gcv7O3MIf5YFG1VUIoOcVpQPHB09G30OoIukndmTMeGcM0UtL7apAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75154947ba9b0c3a1f6c76524c279e9770219b8ef18adbe2d3c244229320f377","last_reissued_at":"2026-07-05T06:32:49.035117Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:32:49.035117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.09994","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":"2026-07-05T06:32:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+p8RqJuT67ewIJMRTlG1W8dXMV6UsB1OIEwhQzP2NbbMgrwh4ntzGbKmQOGBTo2ngwB4f4augxq1wE3ss5+uCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:43:37.672263Z"},"content_sha256":"a2a91a8c52365d20a749b55c08dca87dc63f5031467c983390d614bf0da7225b","schema_version":"1.0","event_id":"sha256:a2a91a8c52365d20a749b55c08dca87dc63f5031467c983390d614bf0da7225b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OUKUSR52TMGDUH3MOZJEYJ46S5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Impact of Disentanglement on Pruning Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.SP"],"primary_cat":"cs.LG","authors_text":"Abd El Rahman Shabayek, Anis Kacem, Carl Shneider, Djamila Aouada, Nilotpal Sinha, Peyman Rostami","submitted_at":"2023-07-19T13:58:01Z","abstract_excerpt":"Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via efficient model compression. Disentangled latent representations produced by variational autoencoder (VAE) networks are a promising approach for achieving model compression because they mainly retain task-specific information, discarding useless information for the task at hand. We make use of the Beta-VAE framework combined with a standard criterion for pruning to investigate "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09994","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.09994/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T06:32:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MZ28Kln2dxhA8kUNe3RQ1H0072hsmb8k4ulsQdkxw9kQNpmkw4G2nGG3WBYgd4DbyPUtZj1XQnLOdGQ3GL8eCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:43:37.672642Z"},"content_sha256":"7fa3b3bf2814ae3bfbf23123649e16156b168e219964a5c88ce3f2ed84ab0ad3","schema_version":"1.0","event_id":"sha256:7fa3b3bf2814ae3bfbf23123649e16156b168e219964a5c88ce3f2ed84ab0ad3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OUKUSR52TMGDUH3MOZJEYJ46S5/bundle.json","state_url":"https://pith.science/pith/OUKUSR52TMGDUH3MOZJEYJ46S5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OUKUSR52TMGDUH3MOZJEYJ46S5/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":"2026-08-13T04:43:37Z","links":{"resolver":"https://pith.science/pith/OUKUSR52TMGDUH3MOZJEYJ46S5","bundle":"https://pith.science/pith/OUKUSR52TMGDUH3MOZJEYJ46S5/bundle.json","state":"https://pith.science/pith/OUKUSR52TMGDUH3MOZJEYJ46S5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OUKUSR52TMGDUH3MOZJEYJ46S5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OUKUSR52TMGDUH3MOZJEYJ46S5","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":"2c9873d1964af5d69ffbdb87a8f80a7e9bb0efaa5ab49258eb53e3716d0fc124","cross_cats_sorted":["cs.CV","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-19T13:58:01Z","title_canon_sha256":"c6edf3f90307cb4522aad71167993ffb41d65a3352a6984a67121e125cba67e8"},"schema_version":"1.0","source":{"id":"2307.09994","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09994","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09994v1","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09994","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"pith_short_12","alias_value":"OUKUSR52TMGD","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"pith_short_16","alias_value":"OUKUSR52TMGDUH3M","created_at":"2026-07-05T06:32:49Z"},{"alias_kind":"pith_short_8","alias_value":"OUKUSR52","created_at":"2026-07-05T06:32:49Z"}],"graph_snapshots":[{"event_id":"sha256:7fa3b3bf2814ae3bfbf23123649e16156b168e219964a5c88ce3f2ed84ab0ad3","target":"graph","created_at":"2026-07-05T06:32:49Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2307.09994/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via efficient model compression. Disentangled latent representations produced by variational autoencoder (VAE) networks are a promising approach for achieving model compression because they mainly retain task-specific information, discarding useless information for the task at hand. We make use of the Beta-VAE framework combined with a standard criterion for pruning to investigate ","authors_text":"Abd El Rahman Shabayek, Anis Kacem, Carl Shneider, Djamila Aouada, Nilotpal Sinha, Peyman Rostami","cross_cats":["cs.CV","eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-19T13:58:01Z","title":"Impact of Disentanglement on Pruning Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09994","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:a2a91a8c52365d20a749b55c08dca87dc63f5031467c983390d614bf0da7225b","target":"record","created_at":"2026-07-05T06:32:49Z","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":"2c9873d1964af5d69ffbdb87a8f80a7e9bb0efaa5ab49258eb53e3716d0fc124","cross_cats_sorted":["cs.CV","eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-19T13:58:01Z","title_canon_sha256":"c6edf3f90307cb4522aad71167993ffb41d65a3352a6984a67121e125cba67e8"},"schema_version":"1.0","source":{"id":"2307.09994","kind":"arxiv","version":1}},"canonical_sha256":"75154947ba9b0c3a1f6c76524c279e9770219b8ef18adbe2d3c244229320f377","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75154947ba9b0c3a1f6c76524c279e9770219b8ef18adbe2d3c244229320f377","first_computed_at":"2026-07-05T06:32:49.035117Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:32:49.035117Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JoYurlCYpyTrR/wgkK5hmX9T454wl3c3gcv7O3MIf5YFG1VUIoOcVpQPHB09G30OoIukndmTMeGcM0UtL7apAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:32:49.035620Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.09994","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a2a91a8c52365d20a749b55c08dca87dc63f5031467c983390d614bf0da7225b","sha256:7fa3b3bf2814ae3bfbf23123649e16156b168e219964a5c88ce3f2ed84ab0ad3"],"state_sha256":"a820f84e758201ecccae31cff1be47a22734eaaee793a3021a941ebc72cd0173"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A7yAXXLtNewJ5q2eWJD9TP5hkdAl271UdYd2EVIktdync8T/P9Hh/QNHT7+yC6xL9Fs9zOzNOowH1+sD80M6DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:43:37.675518Z","bundle_sha256":"b28f5db101d0ca594d07f52ba777ab44e2b0b8faf1617ba82d8883158727fc2a"}}