{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:LVCM7NYVT7BWT3DTYRS3HEMEVZ","short_pith_number":"pith:LVCM7NYV","canonical_record":{"source":{"id":"1811.11205","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:14:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a75956ff0dc82be9b88097638bf60740b422ef6ba08dac98d7a9fd6807bbb956","abstract_canon_sha256":"4d4dafd1d9388edcc8facd408dfb61b0ea66cb301201f1bb10e6efa4cad17daa"},"schema_version":"1.0"},"canonical_sha256":"5d44cfb7159fc369ec73c465b39184ae72f78d0202cc3399971faaaefacd3274","source":{"kind":"arxiv","id":"1811.11205","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.11205","created_at":"2026-05-17T23:49:45Z"},{"alias_kind":"arxiv_version","alias_value":"1811.11205v2","created_at":"2026-05-17T23:49:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11205","created_at":"2026-05-17T23:49:45Z"},{"alias_kind":"pith_short_12","alias_value":"LVCM7NYVT7BW","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LVCM7NYVT7BWT3DT","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LVCM7NYV","created_at":"2026-05-18T12:32:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:LVCM7NYVT7BWT3DTYRS3HEMEVZ","target":"record","payload":{"canonical_record":{"source":{"id":"1811.11205","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:14:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a75956ff0dc82be9b88097638bf60740b422ef6ba08dac98d7a9fd6807bbb956","abstract_canon_sha256":"4d4dafd1d9388edcc8facd408dfb61b0ea66cb301201f1bb10e6efa4cad17daa"},"schema_version":"1.0"},"canonical_sha256":"5d44cfb7159fc369ec73c465b39184ae72f78d0202cc3399971faaaefacd3274","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:45.032022Z","signature_b64":"/ZAEBWmqhpdNCcIYB8wAfbJacbREeBvo24V8+UeD3c7GgylhmqAQ4cLO/KFKaVsTPbNV90hrx43Y/QLZTIE5Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d44cfb7159fc369ec73c465b39184ae72f78d0202cc3399971faaaefacd3274","last_reissued_at":"2026-05-17T23:49:45.031575Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:45.031575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.11205","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":"2026-05-17T23:49:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Es8wXi5Efkrpn7BDZaUnYAF5XpNTjHJdFzZPFwY4rSI8eODSRb2LHHmK7zLGL2IW1BGUM2fjT9qwPWcePqGlAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T15:34:19.641413Z"},"content_sha256":"1e226c1370b7acd615004262c4c83e3c1bd196f2ab99bbaf11f782498096d45a","schema_version":"1.0","event_id":"sha256:1e226c1370b7acd615004262c4c83e3c1bd196f2ab99bbaf11f782498096d45a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:LVCM7NYVT7BWT3DTYRS3HEMEVZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"You Look Twice: GaterNet for Dynamic Filter Selection in CNNs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Samy Bengio, Si Si, Yang Li, Zhourong Chen","submitted_at":"2018-11-27T19:14:49Z","abstract_excerpt":"The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing. In this paper, we investigate input-dependent dynamic filter selection in deep convolutional neural networks (CNNs). The problem is interesting because the idea of forcing different parts of the model to learn from different types of samples may help us acquire better filters in CNNs, improve the model generalization performance and potentially increase the interpretability of model behavior. We p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11205","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":""},"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":"2026-05-17T23:49:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L05kJEOk3GXzMebz9pJyEXuYTAP4EOOZw33Yf5DGPAjKcfhx8jGsJ5HCj7R6vCOm37Xf1ThGjp8kqmyHdxf2AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T15:34:19.641998Z"},"content_sha256":"fe7b976274a47d78c4ec02c191ea53355e8c4eced5661fcefa2287fb460d2708","schema_version":"1.0","event_id":"sha256:fe7b976274a47d78c4ec02c191ea53355e8c4eced5661fcefa2287fb460d2708"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ/bundle.json","state_url":"https://pith.science/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ/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":"2026-06-02T15:34:19Z","links":{"resolver":"https://pith.science/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ","bundle":"https://pith.science/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ/bundle.json","state":"https://pith.science/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LVCM7NYVT7BWT3DTYRS3HEMEVZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:LVCM7NYVT7BWT3DTYRS3HEMEVZ","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":"4d4dafd1d9388edcc8facd408dfb61b0ea66cb301201f1bb10e6efa4cad17daa","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:14:49Z","title_canon_sha256":"a75956ff0dc82be9b88097638bf60740b422ef6ba08dac98d7a9fd6807bbb956"},"schema_version":"1.0","source":{"id":"1811.11205","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.11205","created_at":"2026-05-17T23:49:45Z"},{"alias_kind":"arxiv_version","alias_value":"1811.11205v2","created_at":"2026-05-17T23:49:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.11205","created_at":"2026-05-17T23:49:45Z"},{"alias_kind":"pith_short_12","alias_value":"LVCM7NYVT7BW","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_16","alias_value":"LVCM7NYVT7BWT3DT","created_at":"2026-05-18T12:32:37Z"},{"alias_kind":"pith_short_8","alias_value":"LVCM7NYV","created_at":"2026-05-18T12:32:37Z"}],"graph_snapshots":[{"event_id":"sha256:fe7b976274a47d78c4ec02c191ea53355e8c4eced5661fcefa2287fb460d2708","target":"graph","created_at":"2026-05-17T23:49:45Z","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":"The concept of conditional computation for deep nets has been proposed previously to improve model performance by selectively using only parts of the model conditioned on the sample it is processing. In this paper, we investigate input-dependent dynamic filter selection in deep convolutional neural networks (CNNs). The problem is interesting because the idea of forcing different parts of the model to learn from different types of samples may help us acquire better filters in CNNs, improve the model generalization performance and potentially increase the interpretability of model behavior. We p","authors_text":"Samy Bengio, Si Si, Yang Li, Zhourong Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:14:49Z","title":"You Look Twice: GaterNet for Dynamic Filter Selection in CNNs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.11205","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":"sha256:1e226c1370b7acd615004262c4c83e3c1bd196f2ab99bbaf11f782498096d45a","target":"record","created_at":"2026-05-17T23:49:45Z","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":"4d4dafd1d9388edcc8facd408dfb61b0ea66cb301201f1bb10e6efa4cad17daa","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-27T19:14:49Z","title_canon_sha256":"a75956ff0dc82be9b88097638bf60740b422ef6ba08dac98d7a9fd6807bbb956"},"schema_version":"1.0","source":{"id":"1811.11205","kind":"arxiv","version":2}},"canonical_sha256":"5d44cfb7159fc369ec73c465b39184ae72f78d0202cc3399971faaaefacd3274","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5d44cfb7159fc369ec73c465b39184ae72f78d0202cc3399971faaaefacd3274","first_computed_at":"2026-05-17T23:49:45.031575Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:45.031575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/ZAEBWmqhpdNCcIYB8wAfbJacbREeBvo24V8+UeD3c7GgylhmqAQ4cLO/KFKaVsTPbNV90hrx43Y/QLZTIE5Cw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:45.032022Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.11205","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1e226c1370b7acd615004262c4c83e3c1bd196f2ab99bbaf11f782498096d45a","sha256:fe7b976274a47d78c4ec02c191ea53355e8c4eced5661fcefa2287fb460d2708"],"state_sha256":"6c209fa9cdfbf790e82533c16bf57ae1da6b7e0477380bbc33ce9c97fb8e2402"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IL4dpChmiyv86d2Fsj6Bl6JSH7cySY8TaH2V6bBNb5sy+x61ZY8i6ZzA1jXiek0YRlbF+2Z7zk14WyU0cjncAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T15:34:19.644901Z","bundle_sha256":"928ca4201934ea1da666cb376900d233f7deca59885f3bf2b217b603ffa5a744"}}