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Nature Machine Intelligence, Volume 2
Volume 2, Number 1, January 2020
- Let's go 2020. 1
- Alexander S. Rich, Cynthia Rudin, David M. P. Jacoby, Robin Freeman, Oliver R. Wearn, Henry Shevlin, Kanta Dihal, Seán S. ÓhÉigeartaigh, James Butcher, Marco Lippi, Przemyslaw Palka, Paolo Torroni
, Shannon Wongvibulsin, Edmon Begoli, Gisbert Schneider, Stephen Cave, Mona Sloane, Emanuel Moss, Iyad Rahwan, Ken Goldberg, David Howard, Luciano Floridi, Jack Stilgoe:
AI reflections in 2019. 2-9 - Andreas Theodorou
Towards ethical and socio-legal governance in AI. 10-12 - Jiuyong Li
Accurate data-driven prediction does not mean high reproducibility. 13-15 - Wojciech Samek
Learning with explainable trees. 16-17 - Maria Littmann
Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. 18-24 - Wenzhi Mao
AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction. 25-33 - Dongwook Lee
Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks. 34-42 - Indranil Chakraborty
Constructing energy-efficient mixed-precision neural networks through principal component analysis for edge intelligence. 43-55 - Scott M. Lundberg, Gabriel G. Erion
From local explanations to global understanding with explainable AI for trees. 56-67 - Toby Howison
Large-scale automated investigation of free-falling paper shapes via iterative physical experimentation. 68-75 - Brett D. Roads
Learning as the unsupervised alignment of conceptual systems. 76-82
Volume 2, Number 2, February 2020
- The only game in town. 83
- Jeffrey L. Furman, Florenta Teodoridis
Machine learning could improve innovation policy. 84 - Nicolas Spatola
The citizen at the centre of ethics. 85 - Abubakar Abid
An online platform for interactive feedback in biomedical machine learning. 86-88 - Rafael A. Calvo
Advancing impact assessment for intelligent systems. 89-91 - Jessica S. Horst, Chris M. Bird:
Conceptual systems align to aid concept learning. 92-93 - Frank Cichos
Machine learning for active matter. 94-103 - Alvin I. Chen
Deep learning robotic guidance for autonomous vascular access. 104-115 - Menglun Wang, Zixuan Cang
A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation. 116-123 - Leonie Zeune
Deep learning of circulating tumour cells. 124-133 - Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu
Predicting drug-protein interaction using quasi-visual question answering system. 134-140 - Qi Yan
Deep-learning-based prediction of late age-related macular degeneration progression. 141-150
Volume 2, Number 3, March 2020
- Into the latent space. 151
- Adam Poulsen
Queering machines. 152 - Trenton A. Jerde:
The path of foundational research in robotics. 153-154 - Luca Manneschi, Eleni Vasilaki
An alternative to backpropagation through time. 155-156 - Jason Kamran Eshraghian
Human ownership of artificial creativity. 157-160 - Man-Fai Ng, Jin Zhao, Qingyu Yan, Gareth John Conduit, Zhi Wei Seh
Predicting the state of charge and health of batteries using data-driven machine learning. 161-170 - Michael Moret
Generative molecular design in low data regimes. 171-180 - Nabil Imam
Rapid online learning and robust recall in a neuromorphic olfactory circuit. 181-191
Volume 2, Number 4, April 2020
- Pandemic data challenges. 193
- Aimun A. B. Jamjoom
Exploring public opinion about liability and responsibility in surgical robotics. 194-196 - Julia Stoyanovich
The imperative of interpretable machines. 197-199 - Benjamin A. Rizkin
Combining automated microfluidic experimentation with machine learning for efficient polymerization design. 200-209 - William Lotter
A neural network trained for prediction mimics diverse features of biological neurons and perception. 210-219 - Ruibang Luo
Exploring the limit of using a deep neural network on pileup data for germline variant calling. 220-227 - Valery Vishnevskiy
Deep variational network for rapid 4D flow MRI reconstruction. 228-235 - Noorul Amin
Author Correction: Evaluation of deep learning in non-coding RNA classification. 236 - Kevin Faust
Publisher Correction: Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning. 237
Volume 2, Number 5, May 2020
- A match for virtual conferences. 239
- Andreagiovanni Reina
Robot teams stay safe with blockchains. 240-241 - Edoardo Sinibaldi
Contributions from the Catholic Church to ethical reflections in the digital era. 242-244 - Bo Pang
Complex sequential understanding through the awareness of spatial and temporal concepts. 245-253 - Panagiotis-Christos Kotsias
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. 254-265 - Oleg S. Pianykh
Improving healthcare operations management with machine learning. 266-273 - Pritam Mukherjee
A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. 274-282 - Li Yan
An interpretable mortality prediction model for COVID-19 patients. 283-288 - Publisher Correction: Pandemic data challenges. 289
Volume 2, Number 6, June 2020
- Finding a role for AI in the pandemic. 291
- José Anchieta C. C. Nunes
Speeding up coral reef conservation with AI-aided automated image analysis. 292 - Nathan Peiffer-Smadja
Machine Learning for COVID-19 needs global collaboration and data-sharing. 293-294 - Miguel A. Luengo-Oroz
Artificial intelligence cooperation to support the global response to COVID-19. 295-297 - Yipeng Hu
The challenges of deploying artificial intelligence models in a rapidly evolving pandemic. 298-300 - Yann Sweeney
Tracking the debate on COVID-19 surveillance tools. 301-304 - Georgios Kaissis, Marcus R. Makowski, Daniel Rueckert
Secure, privacy-preserving and federated machine learning in medical imaging. 305-311 - Fei Wu, Cewu Lu
Towards a new generation of artificial intelligence in China. 312-316 - Changjun Fan, Li Zeng
Finding key players in complex networks through deep reinforcement learning. 317-324 - Stanislaw Wozniak
Deep learning incorporating biologically inspired neural dynamics and in-memory computing. 325-336 - Yong Wang
Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning. 337-346 - Lixiang Hong
A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. 347-355 - Mika Sarkin Jain
Predicting tumour mutational burden from histopathological images using multiscale deep learning. 356-362
Volume 2, Number 7, July 2020
- Technology can't fix this. 363
- Laura Aymerich-Franch:
Why it is time to stop ostracizing social robots. 364 - Asaf Tzachor
Artificial intelligence in a crisis needs ethics with urgency. 365-366 - Jan Hoinka, Teresa M. Przytycka:
Embedding gene sets in low-dimensional space. 367-368 - Mattia Prosperi
Causal inference and counterfactual prediction in machine learning for actionable healthcare. 369-375 - Shinya Tasaki
Deep learning decodes the principles of differential gene expression. 376-386 - Sheng Wang, Emily R. Flynn, Russ B. Altman
Gaussian embedding for large-scale gene set analysis. 387-395 - Dries Sels
Quantum approximate Bayesian computation for NMR model inference. 396-402 - Babak Rahmani
Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media. 403-410 - Yuri Tolkach
High-accuracy prostate cancer pathology using deep learning. 411-418
Volume 2, Number 8, August 2020
- Next chapter in artificial writing. 419
- Edmund R. Hunt
A checklist for safe robot swarms. 420-422 - Payal Dhar
The carbon impact of artificial intelligence. 423-425 - Yunan Luo
When causal inference meets deep learning. 426-427 - Sebastian Risi
Increasing generality in machine learning through procedural content generation. 428-436 - Hiroyuki Suzuki
Origami-inspired miniature manipulator for teleoperated microsurgery. 437-446 - Qin Cao
A unified framework for integrative study of heterogeneous gene regulatory mechanisms. 447-456 - Wiktor Beker, Agnieszka Wolos
Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks. 457-465 - Fei Tan, Tian Tian
Elucidation of DNA methylation on N6-adenine with deep learning. 466-475 - Patrick Schramowski
Making deep neural networks right for the right scientific reasons by interacting with their explanations. 476-486
Volume 2, Number 9, September 2020
- Algorithms to live by. 487
- Stuart McLennan
An embedded ethics approach for AI development. 488-490 - Shakir Mohamed
Domesticating the techno-racial project. 491 - Hugo Merchant
Estimating time with neural networks. 492-493 - Mauro Birattari
Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms. 494-499 - Yue Cao
Ensemble deep learning in bioinformatics. 500-508 - Kyle Mills
Finding the ground state of spin Hamiltonians with reinforcement learning. 509-517 - Christian Pek
Using online verification to prevent autonomous vehicles from causing accidents. 518-528 - Erwan Le Merrer
Remote explainability faces the bouncer problem. 529-539 - Cen Wan
Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. 540-550 - Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu
Publisher Correction: Predicting drug-protein interaction using quasi-visual question answering system. 551
Volume 2, Number 10, October 2020
- Call to action for robotics. 553
- Bilal A. Mateen
Improving the quality of machine learning in health applications and clinical research. 554-556 - Eliska Greplova:
Solving optimization tasks in condensed matter. 557-558 - Jaron Porciello
Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning. 559-565 - Ajmal Zemmar
The rise of robots in surgical environments during COVID-19. 566-572 - José Jiménez-Luna
Drug discovery with explainable artificial intelligence. 573-584 - Samik Banerjee
Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. 585-594 - James W. Martin
Enabling the future of colonoscopy with intelligent and autonomous magnetic manipulation. 595-606 - Jian Hu, Xiangjie Li, Gang Hu
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. 607-618 - Anthony Culos
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. 619-628 - Marcus Ludwig
Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. 629-641 - Mathias Lechner
Neural circuit policies enabling auditable autonomy. 642-652
Volume 2, Number 11, November 2020
- Materializing artificial intelligence. 653
- Isabella Hermann
Beware of fictional AI narratives. 654 - Jathan Sadowski
More than a few bad apps. 655-657 - Aslan Miriyev
Skills for physical artificial intelligence. 658-660 - Michael Milford
C. Elegans inspires self-driving cars. 661-662 - Dongdong Jin, Li Zhang
Embodied intelligence weaves a better future. 663-664 - Robert Geirhos
Shortcut learning in deep neural networks. 665-673 - Mikhail Genkin, Tatiana A. Engel
Moving beyond generalization to accurate interpretation of flexible models. 674-683 - Jacob Levy Abitbol
Interpretable socioeconomic status inference from aerial imagery through urban patterns. 684-692 - Lifei Wang, Rui Nie, Zeyang Yu, Ruyue Xin, Caihong Zheng, Zhang Zhang, Jiang Zhang
An interpretable deep-learning architecture of capsule networks for identifying cell-type gene expression programs from single-cell RNA-sequencing data. 693-703 - Rohit Batra
Prediction of water stability of metal-organic frameworks using machine learning. 704-710 - Lukas Dekanovsky, Bahareh Khezri
Chemically programmable microrobots weaving a web from hormones. 711-718 - Olle G. Holmberg, Niklas D. Köhler, Thiago Martins, Jakob Siedlecki
Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy. 719-726 - Marcus Ludwig
Publisher Correction: Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. 727
Volume 2, Number 12, December 2020
- Research, reuse, repeat. 729
- Katarzyna Nowaczyk-Basinska
Let's talk about digital death. 730 - Cameron Buckner
Understanding adversarial examples requires a theory of artefacts for deep learning. 731-736 - Ge Wang
Deep learning for tomographic image reconstruction. 737-748 - Somesh Mohapatra
Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks. 749-752 - Daniel Tanneberg
Evolutionary training and abstraction yields algorithmic generalization of neural computers. 753-763 - Ngoc Hieu Tran
Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. 764-771 - Zhi Chen
Concept whitening for interpretable image recognition. 772-782 - David C. Schedl
Search and rescue with airborne optical sectioning. 783-790 - Paul Bertens
Network of evolvable neural units can learn synaptic learning rules and spiking dynamics. 791-799 - Soeren Lukassen
Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders. 800-809 - Jiayun Dong
Exploring the cloud of variable importance for the set of all good models. 810-824
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