{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:IFTGBLXGYLDVRCIBVS73E3KWEZ","short_pith_number":"pith:IFTGBLXG","canonical_record":{"source":{"id":"2306.08021","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-13T15:21:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a87b551aa6e8d2f6c2a356c4bec4eb690e196aa21962376c678cc2c06c28bc7e","abstract_canon_sha256":"a38092d0411e48cec28ed580235c2f66469b853381f19d8ceec2c69914044b2c"},"schema_version":"1.0"},"canonical_sha256":"416660aee6c2c7588901acbfb26d562661be7cc5491fa12d6adb31f1acd5f501","source":{"kind":"arxiv","id":"2306.08021","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.08021","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"arxiv_version","alias_value":"2306.08021v1","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.08021","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"pith_short_12","alias_value":"IFTGBLXGYLDV","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"pith_short_16","alias_value":"IFTGBLXGYLDVRCIB","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"pith_short_8","alias_value":"IFTGBLXG","created_at":"2026-07-05T06:20:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:IFTGBLXGYLDVRCIBVS73E3KWEZ","target":"record","payload":{"canonical_record":{"source":{"id":"2306.08021","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-13T15:21:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a87b551aa6e8d2f6c2a356c4bec4eb690e196aa21962376c678cc2c06c28bc7e","abstract_canon_sha256":"a38092d0411e48cec28ed580235c2f66469b853381f19d8ceec2c69914044b2c"},"schema_version":"1.0"},"canonical_sha256":"416660aee6c2c7588901acbfb26d562661be7cc5491fa12d6adb31f1acd5f501","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:20:46.877773Z","signature_b64":"nFZ8IlgiirVdxBforfJonOFzh5eIsr99YrBvM+zhh+FnuMkcNhVzXiZ3YqQURnzrYX4AlDFQfL2q0qdfTPMeAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"416660aee6c2c7588901acbfb26d562661be7cc5491fa12d6adb31f1acd5f501","last_reissued_at":"2026-07-05T06:20:46.877145Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:20:46.877145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.08021","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":"2026-07-05T06:20:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/mhdlpoNxzZ+JOCvz1Il1Uzou7VysX0mwWvN9Bj7UamS704MKU3xSH+1RWgo0bnN918GX6ihtn+ijCM2timMBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T17:10:35.909030Z"},"content_sha256":"3fdaba34d13b2ae9493687e72304a88211709171dbf518551a8089ebd0c824f8","schema_version":"1.0","event_id":"sha256:3fdaba34d13b2ae9493687e72304a88211709171dbf518551a8089ebd0c824f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:IFTGBLXGYLDVRCIBVS73E3KWEZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Flexible Channel Dimensions for Differentiable Architecture Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ahmet Caner Y\\\"uz\\\"ug\\\"uler, Nikolaos Dimitriadis, Pascal Frossard","submitted_at":"2023-06-13T15:21:38Z","abstract_excerpt":"Finding optimal channel dimensions (i.e., the number of filters in DNN layers) is essential to design DNNs that perform well under computational resource constraints. Recent work in neural architecture search aims at automating the optimization of the DNN model implementation. However, existing neural architecture search methods for channel dimensions rely on fixed search spaces, which prevents achieving an efficient and fully automated solution. In this work, we propose a novel differentiable neural architecture search method with an efficient dynamic channel allocation algorithm to enable a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.08021","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/2306.08021/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":"2026-07-05T06:20:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KAfkdyAPIOQqVSfkrr+vvyTOSGJ3X/V0/sTKWhWJ24b2TLcMsxXSTV4iOUJEHB6spBA7mRkRZG28dD1rDn4uAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T17:10:35.909814Z"},"content_sha256":"377afb12f7b60eea2d5db25d2ddcb137ee958f1b200ee67b58b75ae503f664ac","schema_version":"1.0","event_id":"sha256:377afb12f7b60eea2d5db25d2ddcb137ee958f1b200ee67b58b75ae503f664ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ/bundle.json","state_url":"https://pith.science/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ/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":"2026-08-17T17:10:35Z","links":{"resolver":"https://pith.science/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ","bundle":"https://pith.science/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ/bundle.json","state":"https://pith.science/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IFTGBLXGYLDVRCIBVS73E3KWEZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:IFTGBLXGYLDVRCIBVS73E3KWEZ","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":"a38092d0411e48cec28ed580235c2f66469b853381f19d8ceec2c69914044b2c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-13T15:21:38Z","title_canon_sha256":"a87b551aa6e8d2f6c2a356c4bec4eb690e196aa21962376c678cc2c06c28bc7e"},"schema_version":"1.0","source":{"id":"2306.08021","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.08021","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"arxiv_version","alias_value":"2306.08021v1","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.08021","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"pith_short_12","alias_value":"IFTGBLXGYLDV","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"pith_short_16","alias_value":"IFTGBLXGYLDVRCIB","created_at":"2026-07-05T06:20:46Z"},{"alias_kind":"pith_short_8","alias_value":"IFTGBLXG","created_at":"2026-07-05T06:20:46Z"}],"graph_snapshots":[{"event_id":"sha256:377afb12f7b60eea2d5db25d2ddcb137ee958f1b200ee67b58b75ae503f664ac","target":"graph","created_at":"2026-07-05T06:20:46Z","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/2306.08021/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Finding optimal channel dimensions (i.e., the number of filters in DNN layers) is essential to design DNNs that perform well under computational resource constraints. Recent work in neural architecture search aims at automating the optimization of the DNN model implementation. However, existing neural architecture search methods for channel dimensions rely on fixed search spaces, which prevents achieving an efficient and fully automated solution. In this work, we propose a novel differentiable neural architecture search method with an efficient dynamic channel allocation algorithm to enable a ","authors_text":"Ahmet Caner Y\\\"uz\\\"ug\\\"uler, Nikolaos Dimitriadis, Pascal Frossard","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-13T15:21:38Z","title":"Flexible Channel Dimensions for Differentiable Architecture Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.08021","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":"sha256:3fdaba34d13b2ae9493687e72304a88211709171dbf518551a8089ebd0c824f8","target":"record","created_at":"2026-07-05T06:20:46Z","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":"a38092d0411e48cec28ed580235c2f66469b853381f19d8ceec2c69914044b2c","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-06-13T15:21:38Z","title_canon_sha256":"a87b551aa6e8d2f6c2a356c4bec4eb690e196aa21962376c678cc2c06c28bc7e"},"schema_version":"1.0","source":{"id":"2306.08021","kind":"arxiv","version":1}},"canonical_sha256":"416660aee6c2c7588901acbfb26d562661be7cc5491fa12d6adb31f1acd5f501","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"416660aee6c2c7588901acbfb26d562661be7cc5491fa12d6adb31f1acd5f501","first_computed_at":"2026-07-05T06:20:46.877145Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:20:46.877145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nFZ8IlgiirVdxBforfJonOFzh5eIsr99YrBvM+zhh+FnuMkcNhVzXiZ3YqQURnzrYX4AlDFQfL2q0qdfTPMeAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:20:46.877773Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.08021","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fdaba34d13b2ae9493687e72304a88211709171dbf518551a8089ebd0c824f8","sha256:377afb12f7b60eea2d5db25d2ddcb137ee958f1b200ee67b58b75ae503f664ac"],"state_sha256":"d1efca3bb5db50a47bb4f513d7fc761f54678e7ecc69cf2c43a07cbe811e82b4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zy5Ng7V/XCtZowbroWsoSJd7G0ZEq2C0D3HrQbgz765Q0kDnuwJCf9Rgpdya1A2ZSqYe/JjUhCCizOFiD0SqCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T17:10:35.919050Z","bundle_sha256":"76fadb26e88cd88616c92aa1a24ad8cbfb9725759c6aae9bc81e2d9f757565b3"}}