{"work":{"id":"25da3043-43a2-4bd4-a0d2-e96dc6eec174","openalex_id":null,"doi":null,"arxiv_id":"1511.06279","raw_key":null,"title":"Neural Programmer-Interpreters","authors":null,"authors_text":"Scott Reed and Nando De Freitas","year":2015,"venue":"cs.LG","abstract":"We propose the neural programmer-interpreter (NPI): a recurrent and compositional neural network that learns to represent and execute programs. NPI has three learnable components: a task-agnostic recurrent core, a persistent key-value program memory, and domain-specific encoders that enable a single NPI to operate in multiple perceptually diverse environments with distinct affordances. By learning to compose lower-level programs to express higher-level programs, NPI reduces sample complexity and increases generalization ability compared to sequence-to-sequence LSTMs. The program memory allows efficient learning of additional tasks by building on existing programs. NPI can also harness the environment (e.g. a scratch pad with read-write pointers) to cache intermediate results of computation, lessening the long-term memory burden on recurrent hidden units. In this work we train the NPI with fully-supervised execution traces; each program has example sequences of calls to the immediate subprograms conditioned on the input. Rather than training on a huge number of relatively weak labels, NPI learns from a small number of rich examples. We demonstrate the capability of our model to learn several types of compositional programs: addition, sorting, and canonicalizing 3D models. Furthermore, a single NPI learns to execute these programs and all 21 associated subprograms.","external_url":"https://arxiv.org/abs/1511.06279","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-24T18:24:48.350287+00:00","pith_arxiv_id":"1511.06279","created_at":"2026-05-10T22:20:46.842472+00:00","updated_at":"2026-05-24T18:24:48.350287+00:00","title_quality_ok":false,"display_title":"Neural programmer-interpreters","render_title":"Neural programmer-interpreters"},"hub":{"state":{"work_id":"25da3043-43a2-4bd4-a0d2-e96dc6eec174","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":10,"external_cited_by_count":null,"distinct_field_count":4,"first_pith_cited_at":"2016-03-29T22:09:00+00:00","last_pith_cited_at":"2026-04-28T03:15:44+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":"2026-06-03T08:55:58.917278+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":2}],"polarity_counts":[{"context_polarity":"background","n":2}],"runs":{},"summary":{},"graph":{},"authors":[]}}