Nature Machine Intelligence 2023: all accepted papers
- Machine learning-enabled globally guaranteed evolutionary computation ↗Bin Li, Ziping Wei, Jingjing Wu et al.31 citations
- Neural-network solutions to stochastic reaction networks ↗Ying Tang, Jiayu Weng, Pan Zhang31 citations
- Federated machine learning in data-protection-compliant research ↗Alissa Brauneck, Louisa Schmalhorst, Mohammad Mahdi Kazemi Majdabadi et al.30 citations
- Updating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for reporting AI research ↗Ali Shah Tejani, Michail E. Klontzas, Anthony A. Gatti et al.30 citations
- Evaluating protein binding interfaces with transformer networks ↗Vitalii Stebliankin, Azam Shirali, Prabin Baral et al.29 citations
- Predicting the prevalence of complex genetic diseases from individual genotype profiles using capsule networks ↗Xiao Luo, Xiongbin Kang, Alexander Schönhuth29 citations
- Translating intersectionality to fair machine learning in health sciences ↗Elle Lett, William La Cava28 citations
- Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model ↗Bo Qiang, Yiran Zhou, Yuheng Ding et al.27 citations
- Enabling collaborative governance of medical AI ↗W. Nicholson Price, Mark Sendak, Suresh Balu et al.27 citations
- Prediction of mechanistic subtypes of Parkinson’s using patient-derived stem cell models ↗Karishma D’Sa, James R. Evans, Gurvir S. Virdi et al.27 citations
- Identifying important sensory feedback for learning locomotion skills ↗Wanming Yu, Chuanyu Yang, Christopher McGreavy et al.25 citations
- Reusability report: Learning the transcriptional grammar in single-cell RNA-sequencing data using transformers ↗Sameer Ahmad Khan, Alberto Maillo, Vincenzo Lagani et al.25 citations
- Testing the limits of natural language models for predicting human language judgements ↗Tal Golan, Matthew Siegelman, Nikolaus Kriegeskorte et al.25 citations
- A neural machine code and programming framework for the reservoir computer ↗Jason Z. Kim, Dani S. Bassett24 citations
- Active learning for optimal intervention design in causal models ↗Jiaqi Zhang, Louis V. Cammarata, Chandler Squires et al.24 citations
- Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling ↗Fang Wang, Fan Yang, Long-Kai Huang et al.24 citations
- Mode switching in organisms for solving explore-versus-exploit problems ↗Debojyoti Biswas, Andrew Lamperski, Yu Jin Yang et al.24 citations
- Hierarchical generative modelling for autonomous robots ↗Kai Yuan, Noor Sajid, Karl John Friston et al.23 citations
- Structure-inducing pre-training ↗Matthew B. A. McDermott, Brendan Yap, Peter Szolovits B et al.23 citations
- What’s the next word in large language models? ↗23 citations
- Ethical hazards of health data governance in the metaverse ↗Kristin M. Kostick, Vasiliki Nataly Rahimzadeh22 citations
- Improving Wikipedia verifiability with AI ↗Fabio Petroni, Samuel Broscheit, Aleksandra Piktus et al.20 citations
- Why the European AI Act transparency obligation is insufficient ↗Zihao Li19 citations
- Activity–weight duality in feed-forward neural networks reveals two co-determinants for generalization ↗Yu Feng, Wei Zhang, Yuhai Tu18 citations
- Introducing contextual transparency for automated decision systems ↗Mona Sloane, Ian René Solano-Kamaiko, Jun Yuan et al.18 citations
- The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols ↗Paula Dhiman, Rebecca Whittle, Ben VAN CALSTER et al.18 citations
- Differentiable visual computing for inverse problems and machine learning ↗Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas et al.17 citations
- A typology for exploring the mitigation of shortcut behaviour ↗Felix Friedrich, Wolfgang Stammer, Patrick Schramowski et al.16 citations
- Estimating categorical counterfactuals via deep twin networks ↗Athanasios Vlontzos, Bernhard Kainz, Ciarán M. Lee16 citations
- Supervised learning of high-confidence phenotypic subpopulations from single-cell data ↗Tao Ren, Canping Chen, Alexey Valeryevich Danilov et al.16 citations
- Writing the rules in AI-assisted writing ↗14 citations
- An extension to the FDA approval process is needed to achieve AI equity ↗Alessandro Hammond, Bhav Jain, Leo Anthony Celi et al.12 citations
- Foundation models and the privatization of public knowledge ↗Fabian Ferrari, José van Dijck, Antal P. J. van den Bosch12 citations
- Medical artificial intelligence is as much social as it is technological ↗Annamaria Carusi, Peter Winter, Iain J. Armstrong et al.12 citations
- Meta-learning for T cell receptor binding specificity and beyond ↗Duolin Wang, Fei He, Yang Yu et al.11 citations
- A ‘programming’ framework for recurrent neural networks ↗Manuel Beirán, Camille A. Spencer-Salmon, Kanaka Rajan10 citations
- Life-threatening ventricular arrhythmia detection challenge in implantable cardioverter–defibrillators ↗Zhenge Jia, Dawei Li, Xiaowei Xu et al.10 citations
- Navigating the development challenges in creating complex data systems ↗Sören Dittmer, Michael T. Roberts, Julian Gilbey et al.10 citations
- Reply to: The pitfalls of negative data bias for the T-cell epitope specificity challenge ↗Yicheng Gao, Yuli Gao, Kejing Dong et al.10 citations
- Adversarial competition and collusion in algorithmic markets ↗Luc Rocher, Arnaud J. Tournier, Yves-Alexandre de Montjoye9 citations
- Battling disinformation with cryptography ↗Johannes Sedlmeir, Alexander Rieger, Tamara Roth et al.9 citations
- How to break information cocoons ↗Fernando Pereira dos Santos9 citations
- Phy-Q as a measure for physical reasoning intelligence ↗Cheng Qi Xue, Vimukthini Pinto, Chathura Gamage et al.9 citations
- The incentive gap in data work in the era of large models ↗Katy Ilonka Gero, Payel Das, Pierre Dognin et al.9 citations
- Extrapolating heterogeneous time-series gene expression data using Sagittarius ↗Addie Woicik, Mingxin Zhang, Janelle Chan et al.8 citations
- Topological structure of complex predictions ↗Meng Liu, Tamal K. Dey, David F. Gleich8 citations
- A Global South perspective for ethical algorithms and the State ↗Nandana Sengupta, Vidya Subramanian, Anwesh Mukhopadhyay et al.7 citations
- Reusability report: Evaluating reproducibility and reusability of a fine-tuned model to predict drug response in cancer patient samples ↗Emily So, Fengqing Yu, Bo Wang et al.7 citations
- Taking ethics seriously in AV trajectory planning algorithms ↗Lando Kirchmair, Norbert Paulo7 citations
- DishBrain plays Pong and promises more ↗Joshua Goldwag, Ge Wang6 citations
- A touch of virtual reality ↗5 citations
- Improving image labelling quality ↗Thomas George Day, John M. Simpson, Reza Razavi et al.5 citations
- Space missions out of this world with AI ↗5 citations
- Geometric deep learning of particle motion by MAGIK ↗Bahare Fatemi, Jonathan Halcrow, Khuloud Jaqaman4 citations
- Judging the creative prowess of AI ↗Tanmoy Chakraborty, Sarah Masud4 citations
- Many-body control with reinforcement learning and tensor networks ↗Ying Lü, Shi-Ju Ran4 citations
- Spatially embedded neuromorphic networks ↗Filip Milisav, Bratislav Mišić3 citations
- A challenge for rounded evaluation of recommender systems ↗Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel et al.2 citations
- A challenge for the law and artificial intelligence ↗Thomas Burri2 citations
- Matching algorithms for blood donation ↗Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev et al.2 citations
- Translating single-cell genomics into cell types ↗Jesper N. Tegner2 citations
- A cautionary tale about the adoption of medical AI in Sweden ↗Emilia Niemiec1 citation
- Algorithmic recommendations, anyone? ↗1 citation
- Crowdsourcing to predict RNA degradation and secondary structure ↗David A. Hendrix1 citation
- Deconstructing the generalization gap ↗Andrey Gromov1 citation
- Listening in to perceived speech with contrastive learning ↗Sergey D. Stavisky, Maitreyee Wairagkar1 citation
- Catching up with missing particles ↗Séverine Atis, Lionel M. Agostini
- Deep learning of causal structures in high dimensions under data limitations ↗Kai Lagemann, Christian Lagemann, Bernd Taschler et al.
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