The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Jiayuan Mao and others, “The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision”. arXiv:1904.12584v1 (submitted 26 April 2019). ICLR 2019 conference paper (published 2019).
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- No authorship claim recorded yet. The source records list Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum and Jiajun Wu.
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- The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision arXiv (v1) · published 2019 · Retrieved for the candidate pack on 2026-10-29 (SHA-256 fe8ac827268b24f30384ccde2c3f6b1a6f71d7cdff589ff262b6a43eca0dff3b). arXiv:1904.12584v1 PDF, headed "Published as a conference paper at ICLR 2019".
- arXiv:1904.12584 abstract page arXiv · published 2019 · Retrieved for the candidate pack on 2026-10-29 (SHA-256 65a6a3af601779bfb15b9148d87addf69d59d1aeddf70d5f63ce1395a316a103). Abstract page with submission history
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- The Next Decade in AI: Four Steps Towards Robust Artificial IntelligenceAccording to Marcus (2020), Mao et al. (2019) proposed a hybrid neural net-symbolic system for visual question answering, NS-CL, that surpassed the deep-learning alternatives they examined.2020-02-14 · event · Stated in source · Supported Inspect claim & evidence ↗The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence: document page →
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