{"work":{"id":"ef8254cc-4f2b-4a30-ab04-a00fedd6f52f","openalex_id":null,"doi":null,"arxiv_id":"1412.6614","raw_key":null,"title":"In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning","authors":null,"authors_text":"Behnam Neyshabur, Ryota Tomioka, and Nathan Srebro","year":2014,"venue":"cs.LG","abstract":"We present experiments demonstrating that some other form of capacity control, different from network size, plays a central role in learning multilayer feed-forward networks. We argue, partially through analogy to matrix factorization, that this is an inductive bias that can help shed light on deep learning.","external_url":"https://arxiv.org/abs/1412.6614","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-25T14:00:53.628014+00:00","pith_arxiv_id":"1412.6614","created_at":"2026-05-10T13:40:26.476362+00:00","updated_at":"2026-05-25T14:00:53.628014+00:00","title_quality_ok":true,"display_title":"In search of the real inductive bias: On the role of implicit regularization in deep learning","render_title":"In search of the real inductive bias: On the role of implicit regularization in deep learning"},"hub":{"state":{"work_id":"ef8254cc-4f2b-4a30-ab04-a00fedd6f52f","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":12,"external_cited_by_count":null,"distinct_field_count":3,"first_pith_cited_at":"2016-11-10T22:02:36+00:00","last_pith_cited_at":"2026-05-20T17:00:37+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-06-01T06:33:14.835600+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":2},{"context_role":"other","n":1}],"polarity_counts":[{"context_polarity":"background","n":2},{"context_polarity":"unclear","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}