{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:3GYVATVIGJEPBYJ2VDSVFMQ3G2","short_pith_number":"pith:3GYVATVI","canonical_record":{"source":{"id":"2104.10461","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-04-21T11:12:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"931156fe0186ed5e39405b575196cc13c9d2facf6b4e0888310a5112e21af528","abstract_canon_sha256":"dd8e9392e62006d01e1d101c79ab88e21ed9973d49f0b0d91d8e159d505e803b"},"schema_version":"1.0"},"canonical_sha256":"d9b1504ea83248f0e13aa8e552b21b369118ddc093c2b9d765454626edd3d50f","source":{"kind":"arxiv","id":"2104.10461","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.10461","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"arxiv_version","alias_value":"2104.10461v2","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.10461","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"pith_short_12","alias_value":"3GYVATVIGJEP","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"pith_short_16","alias_value":"3GYVATVIGJEPBYJ2","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"pith_short_8","alias_value":"3GYVATVI","created_at":"2026-07-05T02:34:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:3GYVATVIGJEPBYJ2VDSVFMQ3G2","target":"record","payload":{"canonical_record":{"source":{"id":"2104.10461","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-04-21T11:12:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"931156fe0186ed5e39405b575196cc13c9d2facf6b4e0888310a5112e21af528","abstract_canon_sha256":"dd8e9392e62006d01e1d101c79ab88e21ed9973d49f0b0d91d8e159d505e803b"},"schema_version":"1.0"},"canonical_sha256":"d9b1504ea83248f0e13aa8e552b21b369118ddc093c2b9d765454626edd3d50f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:34:13.355832Z","signature_b64":"EUKw9CKyaR9HAUXPc8ac1Q/c0F0WayyWKx/e/Xx04AmcF6rO+MqQ6VPnRaa+6ZbHwhjfY0Eizv3YFtPjmSvFCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9b1504ea83248f0e13aa8e552b21b369118ddc093c2b9d765454626edd3d50f","last_reissued_at":"2026-07-05T02:34:13.355375Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:34:13.355375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2104.10461","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:34:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WUFHZ+PSnhwk2xjRxHLEToMTxZvY2YpsnO6aJ8RbIxETKGMFPdBd3REOvJYXCAT/pg8boc031LmXD3fC2rilDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:20:42.809718Z"},"content_sha256":"6c18df1deff7a66c996cd46b63d0e506d6ac8366d7bfeade0e4616792a7e4ccf","schema_version":"1.0","event_id":"sha256:6c18df1deff7a66c996cd46b63d0e506d6ac8366d7bfeade0e4616792a7e4ccf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:3GYVATVIGJEPBYJ2VDSVFMQ3G2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Alexandros Iosifidis, Arian Bakhtiarnia, Qi Zhang","submitted_at":"2021-04-21T11:12:35Z","abstract_excerpt":"Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time. Multi-exit architectures allow deep neural networks to terminate their execution early in order to adhere to tight deadlines at the cost of accuracy. To mitigate this cost, in this paper we introduce a novel method called Multi-Exit Curriculum Learning that utilizes curriculum learning, a training strategy for neural networks that imitates human learning by sorting the training samples based on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.10461","kind":"arxiv","version":2},"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/2104.10461/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":"2026-07-05T02:34:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+lUprxMvrmNlDXuyrCNz7nR4VXKnlC7NnMutlLcLaSG8hVIrFG/0qnULQ0z49e+eOdOVrVqrXmPjn2xIP8JzAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T07:20:42.810397Z"},"content_sha256":"f68b906795e3e872702d19f9633ef081a38e8dac4c02c980649dfacd6f447160","schema_version":"1.0","event_id":"sha256:f68b906795e3e872702d19f9633ef081a38e8dac4c02c980649dfacd6f447160"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2/bundle.json","state_url":"https://pith.science/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2/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":"2026-07-07T07:20:42Z","links":{"resolver":"https://pith.science/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2","bundle":"https://pith.science/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2/bundle.json","state":"https://pith.science/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3GYVATVIGJEPBYJ2VDSVFMQ3G2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3GYVATVIGJEPBYJ2VDSVFMQ3G2","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":"dd8e9392e62006d01e1d101c79ab88e21ed9973d49f0b0d91d8e159d505e803b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-04-21T11:12:35Z","title_canon_sha256":"931156fe0186ed5e39405b575196cc13c9d2facf6b4e0888310a5112e21af528"},"schema_version":"1.0","source":{"id":"2104.10461","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2104.10461","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"arxiv_version","alias_value":"2104.10461v2","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2104.10461","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"pith_short_12","alias_value":"3GYVATVIGJEP","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"pith_short_16","alias_value":"3GYVATVIGJEPBYJ2","created_at":"2026-07-05T02:34:13Z"},{"alias_kind":"pith_short_8","alias_value":"3GYVATVI","created_at":"2026-07-05T02:34:13Z"}],"graph_snapshots":[{"event_id":"sha256:f68b906795e3e872702d19f9633ef081a38e8dac4c02c980649dfacd6f447160","target":"graph","created_at":"2026-07-05T02:34:13Z","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/2104.10461/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deploying deep learning services for time-sensitive and resource-constrained settings such as IoT using edge computing systems is a challenging task that requires dynamic adjustment of inference time. Multi-exit architectures allow deep neural networks to terminate their execution early in order to adhere to tight deadlines at the cost of accuracy. To mitigate this cost, in this paper we introduce a novel method called Multi-Exit Curriculum Learning that utilizes curriculum learning, a training strategy for neural networks that imitates human learning by sorting the training samples based on t","authors_text":"Alexandros Iosifidis, Arian Bakhtiarnia, Qi Zhang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-04-21T11:12:35Z","title":"Improving the Accuracy of Early Exits in Multi-Exit Architectures via Curriculum Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2104.10461","kind":"arxiv","version":2},"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":"sha256:6c18df1deff7a66c996cd46b63d0e506d6ac8366d7bfeade0e4616792a7e4ccf","target":"record","created_at":"2026-07-05T02:34:13Z","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":"dd8e9392e62006d01e1d101c79ab88e21ed9973d49f0b0d91d8e159d505e803b","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-04-21T11:12:35Z","title_canon_sha256":"931156fe0186ed5e39405b575196cc13c9d2facf6b4e0888310a5112e21af528"},"schema_version":"1.0","source":{"id":"2104.10461","kind":"arxiv","version":2}},"canonical_sha256":"d9b1504ea83248f0e13aa8e552b21b369118ddc093c2b9d765454626edd3d50f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9b1504ea83248f0e13aa8e552b21b369118ddc093c2b9d765454626edd3d50f","first_computed_at":"2026-07-05T02:34:13.355375Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:34:13.355375Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EUKw9CKyaR9HAUXPc8ac1Q/c0F0WayyWKx/e/Xx04AmcF6rO+MqQ6VPnRaa+6ZbHwhjfY0Eizv3YFtPjmSvFCw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:34:13.355832Z","signed_message":"canonical_sha256_bytes"},"source_id":"2104.10461","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c18df1deff7a66c996cd46b63d0e506d6ac8366d7bfeade0e4616792a7e4ccf","sha256:f68b906795e3e872702d19f9633ef081a38e8dac4c02c980649dfacd6f447160"],"state_sha256":"9fa2081706c34964810877aa5f2165d34e438299ff1ee31db5d1b7d6adadd453"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IlIUiR9IbxDGgPzk9bb/oY16GKfYoKqN9XnHynclC/GCTubSKH8Jhuiov8EwEqs+VfSKB/Xz98vjm/fUul9ECg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T07:20:42.813948Z","bundle_sha256":"8584875b375764f29e32ba4c7af73d9285cb9508f3858df329e965eb6c3b03e8"}}