{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:72E4ABJ6HUZHQ2CGDNN2NLEYNS","short_pith_number":"pith:72E4ABJ6","schema_version":"1.0","canonical_sha256":"fe89c0053e3d327868461b5ba6ac986c8692a457538a3f90abc3c6dff3e1bf78","source":{"kind":"arxiv","id":"2505.17626","version":1},"attestation_state":"computed","paper":{"title":"Leveraging Stochastic Depth Training for Adaptive Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Antonio Carlos Schneider Beck, Guilherme Korol, Jeronimo Castrillon","submitted_at":"2025-05-23T08:36:56Z","abstract_excerpt":"Dynamic DNN optimization techniques such as layer-skipping offer increased adaptability and efficiency gains but can lead to i) a larger memory footprint as in decision gates, ii) increased training complexity (e.g., with non-differentiable operations), and iii) less control over performance-quality trade-offs due to its inherent input-dependent execution. To approach these issues, we propose a simpler yet effective alternative for adaptive inference with a zero-overhead, single-model, and time-predictable inference. Central to our approach is the observation that models trained with Stochasti"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2505.17626","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-23T08:36:56Z","cross_cats_sorted":["cs.AR"],"title_canon_sha256":"54ebdad46882a13d9b21eb9494411cc8dce757a3d1e989dbf2c5cad449bdec50","abstract_canon_sha256":"eee6eedc89b02e3b4475da1e59cce17e16164eae091290ccfc2b0a1ec2a31032"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:29.528479Z","signature_b64":"sXUF/Ewi9aj/pHjBFbR4rNqr5u+Uss81sVju66ZVZI8I//lXCj/gbMQiu4lAOfpZvwI1e4/UNRTgEn0427RDAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe89c0053e3d327868461b5ba6ac986c8692a457538a3f90abc3c6dff3e1bf78","last_reissued_at":"2026-07-05T11:08:29.528051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:29.528051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging Stochastic Depth Training for Adaptive Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AR"],"primary_cat":"cs.LG","authors_text":"Antonio Carlos Schneider Beck, Guilherme Korol, Jeronimo Castrillon","submitted_at":"2025-05-23T08:36:56Z","abstract_excerpt":"Dynamic DNN optimization techniques such as layer-skipping offer increased adaptability and efficiency gains but can lead to i) a larger memory footprint as in decision gates, ii) increased training complexity (e.g., with non-differentiable operations), and iii) less control over performance-quality trade-offs due to its inherent input-dependent execution. To approach these issues, we propose a simpler yet effective alternative for adaptive inference with a zero-overhead, single-model, and time-predictable inference. Central to our approach is the observation that models trained with Stochasti"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17626","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/2505.17626/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2505.17626","created_at":"2026-07-05T11:08:29.528114+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17626v1","created_at":"2026-07-05T11:08:29.528114+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17626","created_at":"2026-07-05T11:08:29.528114+00:00"},{"alias_kind":"pith_short_12","alias_value":"72E4ABJ6HUZH","created_at":"2026-07-05T11:08:29.528114+00:00"},{"alias_kind":"pith_short_16","alias_value":"72E4ABJ6HUZHQ2CG","created_at":"2026-07-05T11:08:29.528114+00:00"},{"alias_kind":"pith_short_8","alias_value":"72E4ABJ6","created_at":"2026-07-05T11:08:29.528114+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS","json":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS.json","graph_json":"https://pith.science/api/pith-number/72E4ABJ6HUZHQ2CGDNN2NLEYNS/graph.json","events_json":"https://pith.science/api/pith-number/72E4ABJ6HUZHQ2CGDNN2NLEYNS/events.json","paper":"https://pith.science/paper/72E4ABJ6"},"agent_actions":{"view_html":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS","download_json":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS.json","view_paper":"https://pith.science/paper/72E4ABJ6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17626&json=true","fetch_graph":"https://pith.science/api/pith-number/72E4ABJ6HUZHQ2CGDNN2NLEYNS/graph.json","fetch_events":"https://pith.science/api/pith-number/72E4ABJ6HUZHQ2CGDNN2NLEYNS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS/action/storage_attestation","attest_author":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS/action/author_attestation","sign_citation":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS/action/citation_signature","submit_replication":"https://pith.science/pith/72E4ABJ6HUZHQ2CGDNN2NLEYNS/action/replication_record"}},"created_at":"2026-07-05T11:08:29.528114+00:00","updated_at":"2026-07-05T11:08:29.528114+00:00"}