📰 Nature Machine Intelligence 2018 — papers

The works of Nature Machine Intelligence published in 2018 that our library holds (publisher: Nature Portfolio): 1 work, listed below with the most cited first; each has a page with our summary, its key points and its references.

Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. Designing neural networks through neuroevolution ↗Kenneth Owen Stanley, Jeff Clune, Joel Lehman et al.689 citations
2. The need for uncertainty quantification in machine-assisted medical decision making ↗Edmon Begoli, Tanmoy Bhattacharya A, Dimitri Kusnezov573 citations
3. Long short-term memory networks in memristor crossbar arrays ↗Can Li, Zhongrui Wang, Mingyi Rao et al.400 citations
4. Robots and the return to collaborative intelligence ↗Ken Y. Goldberg78 citations
5. Causal deconvolution by algorithmic generative models ↗Héctor Zenil, Narsis A. Kiani, Allan A. Zea et al.77 citations
6. Learnability can be undecidable ↗Shai Ben-David, Pavel Hrubeš, Shay Moran et al.77 citations
7. Bridging near- and long-term concerns about AI ↗Stephen Cave, Seán Ó hÉigeartaigh62 citations
8. A role for analogue memory in AI hardware ↗Geoffrey W. Burr22 citations
9. More than machines ↗11 citations
10. Waking up to data challenges ↗Yuanfang Guan5 citations

All works of the year (12)

The most cited first; 1 with our summary link to their page here, the others to the original.

1. Designing neural networks through neuroevolution ↗Kenneth Owen Stanley, Jeff Clune, Joel Lehman et al.689 citations
2. The need for uncertainty quantification in machine-assisted medical decision making ↗Edmon Begoli, Tanmoy Bhattacharya A, Dimitri Kusnezov573 citations
3. Long short-term memory networks in memristor crossbar arrays ↗Can Li, Zhongrui Wang, Mingyi Rao et al.400 citations
4. Robots and the return to collaborative intelligence ↗Ken Y. Goldberg78 citations
5. Causal deconvolution by algorithmic generative models ↗Héctor Zenil, Narsis A. Kiani, Allan A. Zea et al.77 citations
6. Learnability can be undecidable ↗Shai Ben-David, Pavel Hrubeš, Shay Moran et al.77 citations
7. Bridging near- and long-term concerns about AI ↗Stephen Cave, Seán Ó hÉigeartaigh62 citations
8. A role for analogue memory in AI hardware ↗Geoffrey W. Burr22 citations
9. More than machines ↗11 citations
10. Waking up to data challenges ↗Yuanfang Guan5 citations
11. Ask this robot for a helping hand ↗Luke Drnach, Lena H. Ting3 citations
12. Climbing Mount AI ↗James V. Stone1 citation

📄 Works with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

1. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models InsteadNature Machine IntelligenceCynthia Rudin2018 · 10,431 citations

📚 Fields

🖥️Computer Science (1)⚙️Engineering (1)🤝Social Sciences (1)

🧑‍🔬 Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

🏛️ Top institutions

Institutions on the bylines of its works here, by the number of works.

🎓Duke UniversityDurham · United States1 work here

works and citations from OpenAlex and our library.

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