{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:4D2OHLMU4H4Z2ZGDCJABS7H6HF","short_pith_number":"pith:4D2OHLMU","schema_version":"1.0","canonical_sha256":"e0f4e3ad94e1f99d64c31240197cfe397490b37605f2c68d46c2eb4ce53ad956","source":{"kind":"arxiv","id":"1711.11059","version":1},"attestation_state":"computed","paper":{"title":"Gaussian Process Neurons Learn Stochastic Activation Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Marcus Basalla, Patrick van der Smagt, Sebastian Urban","submitted_at":"2017-11-29T19:09:14Z","abstract_excerpt":"We propose stochastic, non-parametric activation functions that are fully learnable and individual to each neuron. Complexity and the risk of overfitting are controlled by placing a Gaussian process prior over these functions. The result is the Gaussian process neuron, a probabilistic unit that can be used as the basic building block for probabilistic graphical models that resemble the structure of neural networks. The proposed model can intrinsically handle uncertainties in its inputs and self-estimate the confidence of its predictions. Using variational Bayesian inference and the central lim"},"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":"1711.11059","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-11-29T19:09:14Z","cross_cats_sorted":["cs.LG","cs.NE"],"title_canon_sha256":"40503c1f41037e0a5332592f6fe62921c965ad44d368e895cada374d570b6c45","abstract_canon_sha256":"be3b062dad8fff09cbb6fc9744a56ec0228b32ac8d1f14fa44c1a43e93987d88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:12.605301Z","signature_b64":"MVuwPmL2jM0AdKHStKysylEBvgAAE/BOc+DP5BuILJ663TziJMHHCqsToTyU21x4zyJXwSiArXGjO+VE/3VWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0f4e3ad94e1f99d64c31240197cfe397490b37605f2c68d46c2eb4ce53ad956","last_reissued_at":"2026-05-18T00:29:12.604670Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:12.604670Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gaussian Process Neurons Learn Stochastic Activation Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.NE"],"primary_cat":"stat.ML","authors_text":"Marcus Basalla, Patrick van der Smagt, Sebastian Urban","submitted_at":"2017-11-29T19:09:14Z","abstract_excerpt":"We propose stochastic, non-parametric activation functions that are fully learnable and individual to each neuron. Complexity and the risk of overfitting are controlled by placing a Gaussian process prior over these functions. The result is the Gaussian process neuron, a probabilistic unit that can be used as the basic building block for probabilistic graphical models that resemble the structure of neural networks. The proposed model can intrinsically handle uncertainties in its inputs and self-estimate the confidence of its predictions. Using variational Bayesian inference and the central lim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.11059","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":"1711.11059","created_at":"2026-05-18T00:29:12.604769+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.11059v1","created_at":"2026-05-18T00:29:12.604769+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.11059","created_at":"2026-05-18T00:29:12.604769+00:00"},{"alias_kind":"pith_short_12","alias_value":"4D2OHLMU4H4Z","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"4D2OHLMU4H4Z2ZGD","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"4D2OHLMU","created_at":"2026-05-18T12:30:58.224056+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/4D2OHLMU4H4Z2ZGDCJABS7H6HF","json":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF.json","graph_json":"https://pith.science/api/pith-number/4D2OHLMU4H4Z2ZGDCJABS7H6HF/graph.json","events_json":"https://pith.science/api/pith-number/4D2OHLMU4H4Z2ZGDCJABS7H6HF/events.json","paper":"https://pith.science/paper/4D2OHLMU"},"agent_actions":{"view_html":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF","download_json":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF.json","view_paper":"https://pith.science/paper/4D2OHLMU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.11059&json=true","fetch_graph":"https://pith.science/api/pith-number/4D2OHLMU4H4Z2ZGDCJABS7H6HF/graph.json","fetch_events":"https://pith.science/api/pith-number/4D2OHLMU4H4Z2ZGDCJABS7H6HF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF/action/storage_attestation","attest_author":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF/action/author_attestation","sign_citation":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF/action/citation_signature","submit_replication":"https://pith.science/pith/4D2OHLMU4H4Z2ZGDCJABS7H6HF/action/replication_record"}},"created_at":"2026-05-18T00:29:12.604769+00:00","updated_at":"2026-05-18T00:29:12.604769+00:00"}