期刊


刊名Nature Machine Intelligence
收藏年代2019～2024



全部

2019 2020 2021 2022 2023 2024

2023, vol.5, no.1 2023, vol.5, no.10 2023, vol.5, no.11 2023, vol.5, no.12 2023, vol.5, no.2 2023, vol.5, no.3
2023, vol.5, no.4 2023, vol.5, no.5 2023, vol.5, no.6 2023, vol.5, no.7 2023, vol.5, no.8 2023, vol.5, no.9

题名作者出版年年卷期
Explaining machine learning models with interactive natural language conversations using TalkToModelSlack Dylan; Krishna Satyapriya; Lakkaraju Himabindu; Singh Sameer20232023, vol.5, no.8
The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocolsDhiman Paula; Whittle Rebecca; Van Calster Ben; Ghassemi Marzyeh; Liu Xiaoxuan; McCradden Melissa D.; Moons Karel G. M.; Riley Richard D.; Collins Gary S.20232023, vol.5, no.8
Self-supervised learning of hologram reconstruction using physics consistencyHuang Luzhe; Chen Hanlong; Liu Tairan; Ozcan Aydogan20232023, vol.5, no.8
Seeking a quantum advantage for machine learning 20232023, vol.5, no.8
Taking ethics seriously in AV trajectory planning algorithmsKirchmair Lando; Paulo Norbert20232023, vol.5, no.8
Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisitionBouchard Catherine; Deschênes Andréanne; Bilodeau Anthony; Turcotte Beno?t; Gagné Christian; Lavoie-Cardinal Flavie; Wiesner Theresa20232023, vol.5, no.8
Enabling collaborative governance of medical AIPrice W. Nicholson; Sendak Mark; Balu Suresh; Singh Karandeep20232023, vol.5, no.8
Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicityAlbert Benjamin Alexander; Yang Yunxiao; Shao Xiaoshan M.; Singh Dipika; Smith Kellie N.; Anagnostou Valsamo; Karchin Rachel20232023, vol.5, no.8
Identifying important sensory feedback for learning locomotion skillsYu Wanming; Yang Chuanyu; McGreavy Christopher; Bellegarda Guillaume; Shafiee Milad; Ijspeert Auke Jan; Li Zhibin; Triantafyllidis Eleftherios20232023, vol.5, no.8
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcareImrie Fergus; Davis Robert; van der Schaar Mihaela20232023, vol.5, no.8
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