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ORCIDNature 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, Virginia Dignum:
Towards ethical and socio-legal governance in AI. 10-12 - Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu:
Accurate data-driven prediction does not mean high reproducibility. 13-15 - Wojciech Samek:
Learning with explainable trees. 16-17 - Maria Littmann, Katharina Selig, Liel Cohen-Lavi, Yotam Frank, Peter Hönigschmid, Evans Kataka, Anja Mösch, Kun Qian, Avihai Ron, Sebastian Schmid, Adam Sorbie, Liran Szlak, Ayana Dagan-Wiener, Nir Ben-Tal, Masha Y. Niv, Daniel Razansky, Björn W. Schuller, Donna P. Ankerst, Tomer Hertz, Burkhard Rost:
Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. 18-24 - Wenzhi Mao, Wenze Ding, Yaoguang Xing, Haipeng Gong:
AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction. 25-33 - Dongwook Lee, Won-Jin Moon, Jong Chul Ye:
Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks. 34-42 - Indranil Chakraborty, Deboleena Roy, Isha Garg, Aayush Ankit, Kaushik Roy:
Constructing energy-efficient mixed-precision neural networks through principal component analysis for edge intelligence. 43-55 - Scott M. Lundberg, Gabriel G. Erion, Hugh Chen, Alex J. DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, Su-In Lee:
From local explanations to global understanding with explainable AI for trees. 56-67 - Toby Howison, Josie Hughes, Fumiya Iida:
Large-scale automated investigation of free-falling paper shapes via iterative physical experimentation. 68-75 - Brett D. Roads, Bradley C. Love:
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, Ali Abdalla, Ali Abid, Dawood Khan, Abdulrahman Alfozan, James Zou:
An online platform for interactive feedback in biomedical machine learning. 86-88 - Rafael A. Calvo, Dorian Peters, Stephen Cave:
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, Kristian Gustavsson, Bernhard Mehlig, Giovanni Volpe:
Machine learning for active matter. 94-103 - Alvin I. Chen, Max L. Balter, Timothy J. Maguire, Martin L. Yarmush:
Deep learning robotic guidance for autonomous vascular access. 104-115 - Menglun Wang, Zixuan Cang, Guo-Wei Wei:
A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation. 116-123 - Leonie Zeune, Yoeri E. Boink, Guus van Dalum, Afroditi Nanou, Sanne de Wit, Kiki C. Andree, Joost F. Swennenhuis, Stephan A. van Gils, Leon W. M. M. Terstappen, Christoph Brune:
Deep learning of circulating tumour cells. 124-133 - Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, Yuedong Yang:
Predicting drug-protein interaction using quasi-visual question answering system. 134-140 - Qi Yan, Daniel E. Weeks, Hongyi Xin, Anand Swaroop, Emily Y. Chew, Heng Huang, Ying Ding, Wei Chen:
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, Eduard Fosch-Villaronga, Roger Andre Søraa:
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, Lukas Friedrich, Francesca Grisoni, Daniel Merk, Gisbert Schneider:
Generative molecular design in low data regimes. 171-180 - Nabil Imam, Thomas A. Cleland:
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, Ammer M. A. Jamjoom, Hani J. Marcus:
Exploring public opinion about liability and responsibility in surgical robotics. 194-196 - Julia Stoyanovich, Jay J. Van Bavel, Tessa V. West:
The imperative of interpretable machines. 197-199 - Benjamin A. Rizkin, Albert S. Shkolnik, Neil J. Ferraro, Ryan L. Hartman:
Combining automated microfluidic experimentation with machine learning for efficient polymerization design. 200-209 - William Lotter, Gabriel Kreiman, David D. Cox:
A neural network trained for prediction mimics diverse features of biological neurons and perception. 210-219 - Ruibang Luo, Chak-Lim Wong, Yat-Sing Wong, Chi Ian Tang, Chi-Man Liu, Chi-Ming Leung, Tak Wah Lam:
Exploring the limit of using a deep neural network on pileup data for germline variant calling. 220-227 - Valery Vishnevskiy, Jonas Walheim, Sebastian Kozerke:
Deep variational network for rapid 4D flow MRI reconstruction. 228-235 - Noorul Amin, Annette McGrath, Yi-Ping Phoebe Chen:
Author Correction: Evaluation of deep learning in non-coding RNA classification. 236 - Kevin Faust, Sudarshan Bala, Randy van Ommeren, Alessia Portante, Raniah Al Qawahmed, Ugljesa Djuric, Phedias Diamandis:
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, Chris Gastmans, Miguel Yáñez, Richard M. Lerner, László Kovács, Carlo Casalone, Renzo Pegoraro, Vincenzo Paglia:
Contributions from the Catholic Church to ethical reflections in the digital era. 242-244 - Bo Pang, Kaiwen Zha, Hanwen Cao, Jiajun Tang, Minghui Yu, Cewu Lu:
Complex sequential understanding through the awareness of spatial and temporal concepts. 245-253 - Panagiotis-Christos Kotsias, Josep Arús-Pous, Hongming Chen, Ola Engkvist, Christian Tyrchan, Esben Jannik Bjerrum:
Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. 254-265 - Oleg S. Pianykh, Steven Guitron, Darren Parke, Chengzhao Richard Zhang, Pari Pandharipande, James Brink, Daniel Rosenthal:
Improving healthcare operations management with machine learning. 266-273 - Pritam Mukherjee, Mu Zhou, Edward Lee, Anne Schicht, Yoganand Balagurunathan, Sandy Napel, Robert J. Gillies, Simon Wong, Alexander Thieme, Ann N. Leung, Olivier Gevaert:
A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets. 274-282 - Li Yan, Hai-Tao Zhang, Jorge M. Gonçalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Liang Jing, Mingyang Zhang, Xiang Huang, Ying Xiao, Haosen Cao, Yanyan Chen, Tongxin Ren, Fang Wang, Yaru Xiao, Sufang Huang, Xi Tan, Niannian Huang, Bo Jiao, Cheng Cheng, Yong Zhang, Ailin Luo, Laurent Mombaerts, Junyang Jin, Zhiguo Cao, Shusheng Li, Hui Xu, Ye Yuan:
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, Igor C. S. Cruz, André Nunes, Hudson Pinheiro:
Speeding up coral reef conservation with AI-aided automated image analysis. 292 - Nathan Peiffer-Smadja, Redwan Maatoug, François-Xavier Lescure, Eric D'ortenzio, Joelle Pineau, Jean-Rémi King:
Machine Learning for COVID-19 needs global collaboration and data-sharing. 293-294 - Miguel A. Luengo-Oroz, Katherine Hoffmann Pham, Joseph Bullock, Robert Kirkpatrick, Alexandra Luccioni, Sasha Rubel, Cedric Wachholz, Moez Chakchouk, Phillippa Biggs, Tim Nguyen, Tina Purnat, Bernardo Mariano:
Artificial intelligence cooperation to support the global response to COVID-19. 295-297 - Yipeng Hu, Joseph Jacob, Geoffrey J. M. Parker, David J. Hawkes, John R. Hurst, Danail Stoyanov:
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, Rickmer Braren:
Secure, privacy-preserving and federated machine learning in medical imaging. 305-311 - Fei Wu, Cewu Lu, Mingjie Zhu, Hao Chen, Jun Zhu, Kai Yu, Lei Li, Ming Li, Qianfeng Chen, Xi Li, Xudong Cao, Zhongyuan Wang, Zhengjun Zha, Yueting Zhuang, Yunhe Pan:
Towards a new generation of artificial intelligence in China. 312-316 - Changjun Fan, Li Zeng, Yizhou Sun, Yang-Yu Liu:
Finding key players in complex networks through deep reinforcement learning. 317-324 - Stanislaw Wozniak, Angeliki Pantazi, Thomas Bohnstingl, Evangelos Eleftheriou:
Deep learning incorporating biologically inspired neural dynamics and in-memory computing. 325-336 - Yong Wang, Mengqi Ji, Shengwei Jiang, Xukang Wang, Jiamin Wu, Feng Duan, Jingtao Fan, Laiqiang Huang, Shaohua Ma, Lu Fang, Qionghai Dai:
Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning. 337-346 - Lixiang Hong, Jinjian Lin, Shuya Li, Fangping Wan, Hui Yang, Tao Jiang, Dan Zhao, Jianyang Zeng:
A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. 347-355 - Mika Sarkin Jain, Tarik F. Massoud:
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, Jess Whittlestone, Lalitha Sundaram, Seán Ó hÉigeartaigh:
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, Yi Guo, Matthew Sperrin, James S. Koopman, Jae S. Min, Xing He, Shannan N. Rich, Mo Wang, Iain E. Buchan, Jiang Bian:
Causal inference and counterfactual prediction in machine learning for actionable healthcare. 369-375 - Shinya Tasaki, Chris Gaiteri, Sara Mostafavi, Yanling Wang:
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, Hesam Dashti, Samia Mora, Olga Demler, Eugene Demler:
Quantum approximate Bayesian computation for NMR model inference. 396-402 - Babak Rahmani, Damien Loterie, Eirini Kakkava, Navid Borhani, Ugur Tegin, Demetri Psaltis, Christophe Moser:
Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media. 403-410 - Yuri Tolkach, Tilmann Dohmgörgen, Marieta Toma, Glen Kristiansen:
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, Sabine Hauert:
A checklist for safe robot swarms. 420-422 - Payal Dhar:
The carbon impact of artificial intelligence. 423-425 - Yunan Luo, Jian Peng, Jianzhu Ma:
When causal inference meets deep learning. 426-427 - Sebastian Risi, Julian Togelius:
Increasing generality in machine learning through procedural content generation. 428-436 - Hiroyuki Suzuki, Robert J. Wood:
Origami-inspired miniature manipulator for teleoperated microsurgery. 437-446 - Qin Cao, Zhenghao Zhang, Alexander Xi Fu, Qiong Wu, Tin-Lap Lee, Eric Lo, Alfred S. L. Cheng, Chao Cheng, Danny Leung, Kevin Y. Yip:
A unified framework for integrative study of heterogeneous gene regulatory mechanisms. 447-456 - Wiktor Beker, Agnieszka Wolos, Sara Szymkuc, Bartosz A. Grzybowski:
Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks. 457-465 - Fei Tan, Tian Tian, Xiurui Hou, Xiang Yu, Lei Gu, Fernanda Mafra, Brian D. Gregory, Zhi Wei, Hakon Hakonarson:
Elucidation of DNA methylation on N6-adenine with deep learning. 466-475 - Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, Kristian Kersting:
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, Amelia Fiske, Leo Anthony Celi, Ruth Müller, Jan Harder, Konstantin Ritt, Sami Haddadin, Alena Buyx:
An embedded ethics approach for AI development. 488-490 - Shakir Mohamed:
Domesticating the techno-racial project. 491 - Hugo Merchant, Oswaldo Pérez:
Estimating time with neural networks. 492-493 - Mauro Birattari, Antoine Ligot, Ken Hasselmann:
Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms. 494-499 - Yue Cao, Thomas Andrew Geddes, Jean Yee Hwa Yang, Pengyi Yang:
Ensemble deep learning in bioinformatics. 500-508 - Kyle Mills, Pooya Ronagh, Isaac Tamblyn:
Finding the ground state of spin Hamiltonians with reinforcement learning. 509-517 - Christian Pek, Stefanie Manzinger, Markus Koschi, Matthias Althoff:
Using online verification to prevent autonomous vehicles from causing accidents. 518-528 - Erwan Le Merrer, Gilles Trédan:
Remote explainability faces the bouncer problem. 529-539 - Cen Wan, David T. Jones:
Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. 540-550 - Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, Yuedong Yang:
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, James Liley, Alastair K. Denniston, Chris C. Holmes, Sebastian J. Vollmer:
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, Maryia Ivanina, Maidul Islam, Stefan Einarson, Haym Hirsh:
Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning. 559-565 - Ajmal Zemmar, Andres M. Lozano, Bradley J. Nelson:
The rise of robots in surgical environments during COVID-19. 566-572 - José Jiménez-Luna, Francesca Grisoni, Gisbert Schneider:
Drug discovery with explainable artificial intelligence. 573-584 - Samik Banerjee, Lucas Magee, Dingkang Wang, Xu Li, Bing-Xing Huo, Jaikishan Jayakumar, Katherine Matho, Meng-Kuan Lin, Keerthi Ram, Mohanasankar Sivaprakasam, Z. Josh Huang, Yusu Wang, Partha P. Mitra:
Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks. 585-594 - James W. Martin, Bruno Scaglioni, Joseph C. Norton, Venkataraman Subramanian, Alberto Arezzo, Keith L. Obstein, Pietro Valdastri:
Enabling the future of colonoscopy with intelligent and autonomous magnetic manipulation. 595-606 - Jian Hu, Xiangjie Li, Gang Hu, Yafei Lyu, Katalin Susztak, Mingyao Li:
Iterative transfer learning with neural network for clustering and cell type classification in single-cell RNA-seq analysis. 607-618 - Anthony Culos, Amy Tsai, Natalie Stanley, Martin Becker, Mohammad Sajjad Ghaemi, David Mcilwain, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Edward Ganio, Laura Peterson, Xiaoyuan Han, Ina A. Stelzer, Kazuo Ando, Dyani Gaudilliere, Thanaphong Phongpreecha, Ivana Maric, Alan L. Chang, Gary M. Shaw, David K. Stevenson, Sean Bendall, Kara L. Davis, Wendy J. Fantl, Garry P. Nolan, Trevor Hastie, Robert Tibshirani, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour:
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. 619-628 - Marcus Ludwig, Louis-Félix Nothias, Kai Dührkop, Irina Koester, Markus Fleischauer, Martin A. Hoffmann, Daniel Petras, Fernando Vargas, Mustafa Morsy, Lihini Aluwihare, Pieter C. Dorrestein, Sebastian Böcker:
Database-independent molecular formula annotation using Gibbs sampling through ZODIAC. 629-641 - Mathias Lechner, Ramin M. Hasani, Alexander Amini, Thomas A. Henzinger, Daniela Rus, Radu Grosu:
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, Mark Andrejevic:
More than a few bad apps. 655-657 - Aslan Miriyev, Mirko Kovac:
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, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann:
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, Márton Karsai:
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, Jun Cai:
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, Carmen Chen, Tania G. Evans, Krista S. Walton, Rampi Ramprasad:
Prediction of water stability of metal-organic frameworks using machine learning. 704-710 - Lukas Dekanovsky, Bahareh Khezri, Zdenka Rottnerova, Filip Novotný, Jan Plutnar, Martin Pumera:
Chemically programmable microrobots weaving a web from hormones. 711-718 - Olle G. Holmberg, Niklas D. Köhler, Thiago Martins, Jakob Siedlecki, Tina Herold, Leonie Keidel, Ben Asani, Johannes Schiefelbein, Siegfried Priglinger, Karsten U. Kortuem, Fabian J. Theis:
Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy. 719-726 - Marcus Ludwig, Louis-Félix Nothias, Kai Dührkop, Irina Koester, Markus Fleischauer, Martin A. Hoffmann, Daniel Petras, Fernando Vargas, Mustafa Morsy, Lihini Aluwihare, Pieter C. Dorrestein, Sebastian Böcker:
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, Stephen Cave:
Let's talk about digital death. 730 - Cameron Buckner:
Understanding adversarial examples requires a theory of artefacts for deep learning. 731-736 - Ge Wang, Jong Chul Ye, Bruno De Man:
Deep learning for tomographic image reconstruction. 737-748 - Somesh Mohapatra, Tzuhsiung Yang, Rafael Gómez-Bombarelli:
Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks. 749-752 - Daniel Tanneberg, Elmar Rueckert, Jan Peters:
Evolutionary training and abstraction yields algorithmic generalization of neural computers. 753-763 - Ngoc Hieu Tran, Rui Qiao, Lei Xin, Xin Chen, Baozhen Shan, Ming Li:
Personalized deep learning of individual immunopeptidomes to identify neoantigens for cancer vaccines. 764-771 - Zhi Chen, Yijie Bei, Cynthia Rudin:
Concept whitening for interpretable image recognition. 772-782 - David C. Schedl, Indrajit Kurmi, Oliver Bimber:
Search and rescue with airborne optical sectioning. 783-790 - Paul Bertens, Seong-Whan Lee:
Network of evolvable neural units can learn synaptic learning rules and spiking dynamics. 791-799 - Soeren Lukassen, Foo Wei Ten, Lukás Adam, Roland Eils, Christian Conrad:
Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders. 800-809 - Jiayun Dong, Cynthia Rudin:
Exploring the cloud of variable importance for the set of all good models. 810-824
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