{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:JVPUO3KXMCFGHKTVJQ4RTHN4GZ","short_pith_number":"pith:JVPUO3KX","schema_version":"1.0","canonical_sha256":"4d5f476d57608a63aa754c39199dbc36798363b79466a02a36c344b5df9d78f2","source":{"kind":"arxiv","id":"1504.04788","version":1},"attestation_state":"computed","paper":{"title":"Compressing Neural Networks with the Hashing Trick","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"James T. Wilson, Kilian Q. Weinberger, Stephen Tyree, Wenlin Chen, Yixin Chen","submitted_at":"2015-04-19T04:24:15Z","abstract_excerpt":"As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket sh"},"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":"1504.04788","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-04-19T04:24:15Z","cross_cats_sorted":["cs.NE"],"title_canon_sha256":"2fd166683a5467aabaddfde473c9de0b2b88c2d040819189a7e6f9d41f153bf2","abstract_canon_sha256":"20813d90142aae3e6e6a35b674e25e8c462ea2374ae71c90f86e526a97fc68fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:24.747651Z","signature_b64":"CSBzdM9J5CdvMOBHhpGxXqc7iwBjqso918TEXhKnqhz/pBAko1uBSUZujWTMZcAzCx9IoUM8+wT0hcDcmPCMCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4d5f476d57608a63aa754c39199dbc36798363b79466a02a36c344b5df9d78f2","last_reissued_at":"2026-05-18T02:18:24.747010Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:24.747010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compressing Neural Networks with the Hashing Trick","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.NE"],"primary_cat":"cs.LG","authors_text":"James T. Wilson, Kilian Q. Weinberger, Stephen Tyree, Wenlin Chen, Yixin Chen","submitted_at":"2015-04-19T04:24:15Z","abstract_excerpt":"As deep nets are increasingly used in applications suited for mobile devices, a fundamental dilemma becomes apparent: the trend in deep learning is to grow models to absorb ever-increasing data set sizes; however mobile devices are designed with very little memory and cannot store such large models. We present a novel network architecture, HashedNets, that exploits inherent redundancy in neural networks to achieve drastic reductions in model sizes. HashedNets uses a low-cost hash function to randomly group connection weights into hash buckets, and all connections within the same hash bucket sh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.04788","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":""},"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":"1504.04788","created_at":"2026-05-18T02:18:24.747125+00:00"},{"alias_kind":"arxiv_version","alias_value":"1504.04788v1","created_at":"2026-05-18T02:18:24.747125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.04788","created_at":"2026-05-18T02:18:24.747125+00:00"},{"alias_kind":"pith_short_12","alias_value":"JVPUO3KXMCFG","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_16","alias_value":"JVPUO3KXMCFGHKTV","created_at":"2026-05-18T12:29:27.538025+00:00"},{"alias_kind":"pith_short_8","alias_value":"JVPUO3KX","created_at":"2026-05-18T12:29:27.538025+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"1510.00149","citing_title":"Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"1704.04861","citing_title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ","json":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ.json","graph_json":"https://pith.science/api/pith-number/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/graph.json","events_json":"https://pith.science/api/pith-number/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/events.json","paper":"https://pith.science/paper/JVPUO3KX"},"agent_actions":{"view_html":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ","download_json":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ.json","view_paper":"https://pith.science/paper/JVPUO3KX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1504.04788&json=true","fetch_graph":"https://pith.science/api/pith-number/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/graph.json","fetch_events":"https://pith.science/api/pith-number/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/action/storage_attestation","attest_author":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/action/author_attestation","sign_citation":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/action/citation_signature","submit_replication":"https://pith.science/pith/JVPUO3KXMCFGHKTVJQ4RTHN4GZ/action/replication_record"}},"created_at":"2026-05-18T02:18:24.747125+00:00","updated_at":"2026-05-18T02:18:24.747125+00:00"}