{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:32OFGUVPUF2Z3O4JAV4NLXM6SS","short_pith_number":"pith:32OFGUVP","schema_version":"1.0","canonical_sha256":"de9c5352afa1759dbb890578d5dd9e9481e47bb9f11e38b443b8b7de4ab4258e","source":{"kind":"arxiv","id":"2010.14019","version":1},"attestation_state":"computed","paper":{"title":"Know Where To Drop Your Weights: Towards Faster Uncertainty Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Akshatha Kamath, Dwaraknath Gnaneshwar, Matias Valdenegro-Toro","submitted_at":"2020-10-27T02:56:27Z","abstract_excerpt":"Estimating epistemic uncertainty of models used in low-latency applications and Out-Of-Distribution samples detection is a challenge due to the computationally demanding nature of uncertainty estimation techniques. Estimating model uncertainty using approximation techniques like Monte Carlo Dropout (MCD), DropConnect (MCDC) requires a large number of forward passes through the network, rendering them inapt for low-latency applications. We propose Select-DC which uses a subset of layers in a neural network to model epistemic uncertainty with MCDC. Through our experiments, we show a significant "},"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":"2010.14019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-10-27T02:56:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"f463862d55fde3bb8eed3d7ff4683a18e3a2d0790ea30bf6fc394b32550ba43d","abstract_canon_sha256":"2d06d985faf806f60f86050bf49fa3c1ac51e2cb9eea45fe78e4123718fbc43c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:46:30.407666Z","signature_b64":"jAQ4EDIARYd/5pnpSjsqMPfAO6eWmiNWzmt7m8WpdvO10Mbp/OFPT8KvuTi7y9ujMsvdfXs5Oz3o0mjDC3qXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de9c5352afa1759dbb890578d5dd9e9481e47bb9f11e38b443b8b7de4ab4258e","last_reissued_at":"2026-07-05T01:46:30.407134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:46:30.407134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Know Where To Drop Your Weights: Towards Faster Uncertainty Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Akshatha Kamath, Dwaraknath Gnaneshwar, Matias Valdenegro-Toro","submitted_at":"2020-10-27T02:56:27Z","abstract_excerpt":"Estimating epistemic uncertainty of models used in low-latency applications and Out-Of-Distribution samples detection is a challenge due to the computationally demanding nature of uncertainty estimation techniques. Estimating model uncertainty using approximation techniques like Monte Carlo Dropout (MCD), DropConnect (MCDC) requires a large number of forward passes through the network, rendering them inapt for low-latency applications. We propose Select-DC which uses a subset of layers in a neural network to model epistemic uncertainty with MCDC. Through our experiments, we show a significant "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.14019","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/2010.14019/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":"2010.14019","created_at":"2026-07-05T01:46:30.407194+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.14019v1","created_at":"2026-07-05T01:46:30.407194+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.14019","created_at":"2026-07-05T01:46:30.407194+00:00"},{"alias_kind":"pith_short_12","alias_value":"32OFGUVPUF2Z","created_at":"2026-07-05T01:46:30.407194+00:00"},{"alias_kind":"pith_short_16","alias_value":"32OFGUVPUF2Z3O4J","created_at":"2026-07-05T01:46:30.407194+00:00"},{"alias_kind":"pith_short_8","alias_value":"32OFGUVP","created_at":"2026-07-05T01:46:30.407194+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/32OFGUVPUF2Z3O4JAV4NLXM6SS","json":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS.json","graph_json":"https://pith.science/api/pith-number/32OFGUVPUF2Z3O4JAV4NLXM6SS/graph.json","events_json":"https://pith.science/api/pith-number/32OFGUVPUF2Z3O4JAV4NLXM6SS/events.json","paper":"https://pith.science/paper/32OFGUVP"},"agent_actions":{"view_html":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS","download_json":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS.json","view_paper":"https://pith.science/paper/32OFGUVP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.14019&json=true","fetch_graph":"https://pith.science/api/pith-number/32OFGUVPUF2Z3O4JAV4NLXM6SS/graph.json","fetch_events":"https://pith.science/api/pith-number/32OFGUVPUF2Z3O4JAV4NLXM6SS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS/action/storage_attestation","attest_author":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS/action/author_attestation","sign_citation":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS/action/citation_signature","submit_replication":"https://pith.science/pith/32OFGUVPUF2Z3O4JAV4NLXM6SS/action/replication_record"}},"created_at":"2026-07-05T01:46:30.407194+00:00","updated_at":"2026-07-05T01:46:30.407194+00:00"}