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ORCIDNature Machine Intelligence, Volume 3
Volume 3, Number 1, January 2021
- Room for improvement. 1
- Anna Jobin, Kingson Man, Antonio Damasio, Georgios Kaissis, Rickmer Braren, Julia Stoyanovich, Jay J. Van Bavel, Tessa V. West, Brent D. Mittelstadt, Jason Eshraghian
, Marta R. Costa-jussà, Asaf Tzachor, Aimun A. B. Jamjoom, Mariarosaria Taddeo, Edoardo Sinibaldi, Yipeng Hu, Miguel A. Luengo-Oroz:
AI reflections in 2020. 2-8 - Risto Miikkulainen, Stephanie Forrest:
A biological perspective on evolutionary computation. 9-15 - Daniele Roberto Giacobbe:
Clinical interpretation of an interpretable prognostic model for patients with COVID-19. 16 - Ye Yuan, Jorge M. Gonçalves, Yan Xiao, Hai-Tao Zhang, Hui Xu, Zhiguo Cao:
Reply to: Clinical interpretation of an interpretable prognostic model for patients with COVID-19. 17 - Janice L. V. Reeve, Patrick J. Twomey:
Consider laboratory aspects in developing patient prediction models. 18 - Li Yan, Jorge M. Gonçalves, Hai-Tao Zhang, Shusheng Li, Ye Yuan:
Reply to: Consider the laboratory aspects in developing patient prediction models. 19 - Claire Dupuis, E. De Montmollin, Mathilde Neuville, B. Mourvillier, S. Ruckly, Jean-François Timsit:
Limited applicability of a COVID-19 specific mortality prediction rule to the intensive care setting. 20-22 - Marian J. R. Quanjel, Thijs C. van Holten, Pieternel C. Gunst-van der Vliet, Jette Wielaard, Bekir Karakaya, Maaike Söhne, Hazra S. Moeniralam, Jan C. Grutters:
Replication of a mortality prediction model in Dutch patients with COVID-19. 23-24 - Matthew A. Barish, Siavash Bolourani, Lawrence F. Lau, Sareen Shah, Theodoros P. Zanos:
External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19. 25-27 - Jorge M. Gonçalves, Li Yan, Hai-Tao Zhang, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Zhiguo Cao, Shusheng Li, Hui Xu, Cheng Cheng, Junyang Jin, Ye Yuan:
Li Yan et al. reply. 28-32 - Guido C. H. E. de Croon, Christophe De Wagter, Tobias Seidl:
Enhancing optical-flow-based control by learning visual appearance cues for flying robots. 33-41 - Antoine Toisoul, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, Maja Pantic:
Estimation of continuous valence and arousal levels from faces in naturalistic conditions. 42-50 - Dylan S. Shah, Joshua P. Powers, Liana G. Tilton, Sam Kriegman, Josh C. Bongard, Rebecca Kramer-Bottiglio:
A soft robot that adapts to environments through shape change. 51-59 - Siyuan Liu, Kim-Han Thung, Liangqiong Qu, Weili Lin, Dinggang Shen, Pew-Thian Yap:
Learning MRI artefact removal with unpaired data. 60-67 - Ruoqi Liu, Lai Wei, Ping Zhang:
A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. 68-75 - Zhenpeng Yao, Benjamín Sánchez-Lengeling, N. Scott Bobbitt, Benjamin J. Bucior, Sai Govind Hari Kumar, Sean P. Collins, Thomas Burns, Tom K. Woo, Omar K. Farha, Randall Q. Snurr, Alán Aspuru-Guzik:
Inverse design of nanoporous crystalline reticular materials with deep generative models. 76-86 - Guido Novati, Hugues Lascombes de Laroussilhe, Petros Koumoutsakos:
Automating turbulence modelling by multi-agent reinforcement learning. 87-96 - Yong Wang, Mengqi Ji, Shengwei Jiang, Xukang Wang, Jiamin Wu, Feng Duan, Jingtao Fan, Laiqiang Huang, Shaohua Ma, Lu Fang, Qionghai Dai:
Author Correction: Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning. 97 - Guido Novati, Hugues Lascombes de Laroussilhe, Petros Koumoutsakos:
Publisher Correction: Automating turbulence modelling by multi-agent reinforcement learning. 98 - Guido Novati, Hugues Lascombes de Laroussilhe, Petros Koumoutsakos:
Publisher Correction: Automating turbulence modelling by multi-agent reinforcement learning. 99
Volume 3, Number 2, February 2021
- Listen to this. 101
- Jonas Boström:
Transformers for future medicinal chemists. 102-103 - Carina E. A. Prunkl, Carolyn Ashurst, Markus Anderljung, Helena Webb, Jan Leike, Allan Dafoe:
Institutionalizing ethics in AI through broader impact requirements. 104-110 - Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo, Luciano Floridi:
A definition, benchmark and database of AI for social good initiatives. 111-115 - Daniel Ahmed, Alexander Sukhov, David Hauri, Dubon Rodrigue, Gian Maranta, Jens Harting, Bradley J. Nelson:
Bioinspired acousto-magnetic microswarm robots with upstream motility. 116-124 - Rens van de Schoot, Jonathan de Bruin, Raoul Schram, Parisa Zahedi, Jan de Boer, Felix Weijdema, Bianca Kramer, Martijn Huijts, Maarten Hoogerwerf, Gerbrich Ferdinands, Albert Harkema, Joukje Willemsen, Yongchao Ma, Qixiang Fang, Sybren Hindriks, Lars Tummers, Daniel L. Oberski:
An open source machine learning framework for efficient and transparent systematic reviews. 125-133 - Deepak Baby, Arthur Van Den Broucke, Sarah Verhulst:
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications. 134-143 - Philippe Schwaller, Daniel Probst, Alain C. Vaucher, Vishnu H. Nair, David Kreutter, Teodoro Laino, Jean-Louis Reymond:
Mapping the space of chemical reactions using attention-based neural networks. 144-152 - Shigeyuki Matsumoto, Shoichi Ishida, Mitsugu Araki, Takayuki Kato, Kei Terayama, Yasushi Okuno:
Extraction of protein dynamics information from cryo-EM maps using deep learning. 153-160 - Zhengyang Wang, Yaochen Xie, Shuiwang Ji:
Global voxel transformer networks for augmented microscopy. 161-171 - An Zheng, Michael Lamkin, Hanqing Zhao, Cynthia Wu, Hao Su, Melissa Gymrek:
Deep neural networks identify sequence context features predictive of transcription factor binding. 172-180 - Siyuan Liu, Kim-Han Thung, Liangqiong Qu, Weili Lin, Dinggang Shen, Pew-Thian Yap:
Publisher Correction: Learning MRI artefact removal with unpaired data. 181 - Itai Orr, Moshik Cohen, Zeev Zalevsky:
Author Correction: High-resolution radar road segmentation using weakly supervised learning. 182
Volume 3, Number 3, March 2021
- AI, COVID-19 and the long haul. 183
- Hao Su, Antonio Di Lallo, Robin R. Murphy, Russell H. Taylor, Brian T. Garibaldi, Axel Krieger:
Physical human-robot interaction for clinical care in infectious environments. 184-186 - Vidushi Marda, Shivangi Narayan:
On the importance of ethnographic methods in AI research. 187-189 - Laurel H. Carney:
Speeding up machine hearing. 190-191 - Irina Higgins:
Generalizing universal function approximators. 192-193 - Tara J. Hamilton:
The best of both worlds. 194-195 - Noorul Amin, Annette McGrath, Yi-Ping Phoebe Chen:
Reply to: LncADeep performance on full-length transcripts. 196 - Cheng Yang, Man Zhou, Haoling Xie, Huaiqiu Zhu:
LncADeep performance on full-length transcripts. 197-198 - Michael Roberts, Derek Driggs, Matthew Thorpe, Julian D. Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, Carola-Bibiane Schönlieb:
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. 199-217 - Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis:
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. 218-229 - Christoph Stöckl, Wolfgang Maass:
Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. 230-238 - Itai Orr, Moshik Cohen, Zeev Zalevsky:
High-resolution radar road segmentation using weakly supervised learning. 239-246 - Thai-Hoang Pham, Yue Qiu, Jucheng Zeng, Lei Xie, Ping Zhang:
A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing. 247-257 - Peter K. Koo, Matt Ploenzke:
Improving representations of genomic sequence motifs in convolutional networks with exponential activations. 258-266 - Yoseob Han, Jaeduck Jang, Eunju Cha, Junho Lee, Hyungjin Chung, Myoungho Jeong, Tae-Gon Kim, Byeong Gyu Chae, Hee Goo Kim, Shinae Jun, Sungwoo Hwang, Eunha Lee, Jong Chul Ye:
Deep learning STEM-EDX tomography of nanocrystals. 267-274
Volume 3, Number 4, April 2021
- People have the AI power. 275
- Ross D. King, Oghenejokpeme I. Orhobor, Charles C. Taylor:
Cross-validation is safe to use. 276 - Alejandro A. Franco:
Escape from flatland. 277-278 - Rohit Bhargava, Kianoush Falahkheirkhah:
Enhancing hyperspectral imaging. 279-280 - Daniel J. Gauthier, Ingo Fischer:
Predicting hidden structure in dynamical systems. 281-282 - Boris Babic, Sara Gerke, Theodoros Evgeniou, I. Glenn Cohen:
Direct-to-consumer medical machine learning and artificial intelligence applications. 283-287 - Edward Korot, Zeyu Guan, Daniel Ferraz, Siegfried K. Wagner, Gongyu Zhang, Xiaoxuan Liu, Livia Faes, Nikolas Pontikos, Samuel G. Finlayson, Hagar Khalid, Gabriella Moraes, Konstantinos Balaskas, Alastair K. Denniston, Pearse A. Keane:
Code-free deep learning for multi-modality medical image classification. 288-298 - Steve Kench, Samuel J. Cooper:
Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. 299-305 - Bryce Manifold, Shuaiqian Men, Ruoqian Hu, Dan Fu:
A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. 306-315 - Jason Z. Kim, Zhixin Lu, Erfan Nozari, George J. Pappas, Danielle S. Bassett:
Teaching recurrent neural networks to infer global temporal structure from local examples. 316-323 - Donatas Repecka, Vykintas Jauniskis, Laurynas Karpus, Elzbieta Rembeza, Irmantas Rokaitis, Jan Zrimec, Simona Poviloniene, Audrius Laurynenas, Sandra Viknander, Wissam Abuajwa, Otto Savolainen, Rolandas Meskys, Martin K. M. Engqvist, Aleksej Zelezniak:
Expanding functional protein sequence spaces using generative adversarial networks. 324-333 - Wan Xiang Shen, Xian Zeng, Feng Zhu, Ya li Wang, Chu Qin, Ying Tan, Yu Yang Jiang, Yuzong Chen:
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations. 334-343 - Lauri Salmela, Nikolaos Tsipinakis, Alessandro Foi, Cyril Billet, John M. Dudley, Goëry Genty:
Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. 344-354 - Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller, Frederick Klauschen:
Morphological and molecular breast cancer profiling through explainable machine learning. 355-366
Volume 3, Number 5, May 2021
- How to be responsible in AI publication. 367
- Eduard Fosch-Villaronga, Pranav Khanna, Hadassah Drukarch, Bart H. M. Custers:
A human in the loop in surgery automation. 368-369 - Liesbeth Venema:
Defining a role for AI ethics in national security. 370-371 - Yi Zhang, Yang Liu, X. Shirley Liu:
Neural network architecture search with AMBER. 372-373 - Shangying Wang, Simone Bianco:
Linking the length scales. 374-375 - Marta R. Costa-jussà:
Towards universal translation. 376-377 - Partha P. Mitra:
Fitting elephants in modern machine learning by statistically consistent interpolation. 378-386 - Ugur Tegin, Niyazi Ulas Dinç, Christophe Moser, Demetri Psaltis:
Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network. 387-391 - Zijun Zhang, Christopher Y. Park, Chandra L. Theesfeld, Olga G. Troyanskaya:
An automated framework for efficiently designing deep convolutional neural networks in genomics. 392-400 - Harsh Bhatia, Timothy S. Carpenter, Helgi I. Ingólfsson, Gautham Dharuman, Piyush Karande, Shusen Liu, Tomas Oppelstrup, Chris Neale, Felice C. Lightstone, Brian Van Essen, James N. Glosli, Peer-Timo Bremer:
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations. 401-409 - Tønnes F. Nygaard, Charles P. Martin, Jim Tørresen, Kyrre Glette, David Howard:
Real-world embodied AI through a morphologically adaptive quadruped robot. 410-419 - Rui Qiao, Ngoc Hieu Tran, Lei Xin, Xin Chen, Ming Li, Baozhen Shan, Ali Ghodsi:
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices. 420-425 - Zixuan Song, Jun Li:
Variable selection with false discovery rate control in deep neural networks. 426-433 - Takuya Isomura, Taro Toyoizumi:
Dimensionality reduction to maximize prediction generalization capability. 434-446 - Darius Roman, Saurabh Saxena, Valentin Robu, Michael G. Pecht, David Flynn:
Machine learning pipeline for battery state-of-health estimation. 447-456 - Takuya Isomura, Taro Toyoizumi:
Publisher Correction: Dimensionality reduction to maximize prediction generalization capability. 457
Volume 3, Number 6, June 2021
- Collaborative learning without sharing data. 459
- Supriya Kapur:
Reducing racial bias in AI models for clinical use requires a top-down intervention. 460 - Abubakar Abid, Maheen Farooqi, James Zou:
Large language models associate Muslims with violence. 461-463 - Ania Korsunska, David C. Fajgenbaum:
Back to the future with machine learning. 464-465 - Silvia Milano, Brent D. Mittelstadt, Sandra Wachter, Christopher Russell:
Epistemic fragmentation poses a threat to the governance of online targeting. 466-472 - Georgios Kaissis, Alexander Ziller, Jonathan Passerat-Palmbach, Théo Ryffel, Dmitrii Usynin, Andrew Trask, Ionésio Lima, Jason Mancuso, Friederike Jungmann, Marc-Matthias Steinborn, Andreas Saleh, Marcus R. Makowski, Daniel Rueckert, Rickmer Braren:
End-to-end privacy preserving deep learning on multi-institutional medical imaging. 473-484 - Alessandra Toniato, Philippe Schwaller, Antonio Cardinale, Joppe Geluykens, Teodoro Laino:
Unassisted noise reduction of chemical reaction datasets. 485-494 - Biagio Brattoli, Uta Büchler, Michael Dorkenwald, Philipp Reiser, Linard Filli, Fritjof Helmchen, Anna-Sophia Wahl, Björn Ommer:
Unsupervised behaviour analysis and magnification (uBAM) using deep learning. 495-506 - Xiaoyan Yin, Rolf Müller:
Integration of deep learning and soft robotics for a biomimetic approach to nonlinear sensing. 507-512 - Roman Schulte-Sasse, Stefan Budach, Denes Hnisz, Annalisa Marsico:
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. 513-526 - Govinda B. Kc, Giovanni Bocci, Srijan Verma, Md Mahmudulla Hassan, Jayme Holmes, Jeremy J. Yang, Suman Sirimulla, Tudor I. Oprea:
A machine learning platform to estimate anti-SARS-CoV-2 activities. 527-535 - Qiao Liu, Shengquan Chen, Rui Jiang, Wing Hung Wong:
Simultaneous deep generative modelling and clustering of single-cell genomic data. 536-544 - Enrica Soria, Fabrizio Schiano, Dario Floreano:
Predictive control of aerial swarms in cluttered environments. 545-554
Volume 3, Number 7, July 2021
- AI on the beach. 555
- Zachary S. Ballard, Calvin Brown, Asad M. Madni, Aydogan Ozcan:
Machine learning and computation-enabled intelligent sensor design. 556-565 - Gregory Falco, Ben Shneiderman, Julia Badger, Ryan Carrier, Anton Dahbura, David Danks, Martin Eling, Alwyn Goodloe, Jerry Gupta, Christopher Hart, Marina Jirotka, Henric Johnson, Cara Lapointe, Ashley J. Llorens, Alan K. Mackworth, Carsten Maple, Sigurður Emil Pálsson, Frank Pasquale, Alan F. T. Winfield, Zee Kin Yeong:
Governing AI safety through independent audits. 566-571 - Jon Paul Janet, Anna Tomberg, Jonas Boström:
Reusability report: Learning the language of synthetic methods used in medicinal chemistry. 572-575 - Yijun Bao, Somayyeh Soltanian-Zadeh, Sina Farsiu, Yiyang Gong:
Segmentation of neurons from fluorescence calcium recordings beyond real time. 590-600 - Jinbo Xu, Matthew McPartlon, Jin Li:
Improved protein structure prediction by deep learning irrespective of co-evolution information. 601-609 - Alex J. DeGrave, Joseph D. Janizek, Su-In Lee:
AI for radiographic COVID-19 detection selects shortcuts over signal. 610-619 - Gabriel G. Erion, Joseph D. Janizek, Pascal Sturmfels, Scott M. Lundberg, Su-In Lee:
Improving performance of deep learning models with axiomatic attribution priors and expected gradients. 620-631 - Brodie Fischbacher, Sarita Hedaya, Brigham J. Hartley, Zhongwei Wang, Gregory Lallos, Dillion Hutson, Matthew Zimmer, Jacob Brammer, Daniel Paull:
Modular deep learning enables automated identification of monoclonal cell lines. 632-640 - Christian Lagemann, Kai Lagemann, Sach Mukherjee, Wolfgang Schröder:
Deep recurrent optical flow learning for particle image velocimetry data. 641-651 - Ania Korsunska, David C. Fajgenbaum:
Publisher Correction: Back to the future with machine learning. 652
Volume 3, Number 8, August 2021
- Striving for health equity with machine learning. 653
- Ashley Nunes, Kay W. Axhausen:
Road safety, health inequity and the imminence of autonomous vehicles. 654-655 - David Rousseau:
Resource-efficient inference for particle physics. 656-657 - David C. Parkes:
Playing with symmetry with neural networks. 658 - Vishwali Mhasawade, Yuan Zhao, Rumi Chunara:
Machine learning and algorithmic fairness in public and population health. 659-666 - Christopher Irrgang, Niklas Boers, Maike Sonnewald, Elizabeth A. Barnes, Christopher Kadow, Joanna Staneva, Jan Saynisch-Wagner:
Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. 667-674 - Claudionor José Nunes Coelho Jr., Aki Kuusela, Shan Li, Hao Zhuang, Jennifer Ngadiuba, Thea Klaeboe Aarrestad, Vladimir Loncar, Maurizio Pierini, Adrian Alan Pol, Sioni Summers:
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors. 675-686 - Martin Bichler, Maximilian Fichtl, Stefan Heidekrüger, Nils Kohring, Paul Sutterer:
Learning equilibria in symmetric auction games using artificial neural networks. 687-695