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Nature 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
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
Reply to: Clinical interpretation of an interpretable prognostic model for patients with COVID-19. 17 - Janice L. V. Reeve
Consider laboratory aspects in developing patient prediction models. 18 - Li Yan
Reply to: Consider the laboratory aspects in developing patient prediction models. 19 - Claire Dupuis
Limited applicability of a COVID-19 specific mortality prediction rule to the intensive care setting. 20-22 - Marian J. R. Quanjel
Replication of a mortality prediction model in Dutch patients with COVID-19. 23-24 - Matthew A. Barish
External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19. 25-27 - Jorge M. Gonçalves
Li Yan et al. reply. 28-32 - Guido C. H. E. de Croon
Enhancing optical-flow-based control by learning visual appearance cues for flying robots. 33-41 - Antoine Toisoul
Estimation of continuous valence and arousal levels from faces in naturalistic conditions. 42-50 - Dylan S. Shah
A soft robot that adapts to environments through shape change. 51-59 - Siyuan Liu
Learning MRI artefact removal with unpaired data. 60-67 - Ruoqi Liu
A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data. 68-75 - Zhenpeng Yao
Inverse design of nanoporous crystalline reticular materials with deep generative models. 76-86 - Guido Novati
Automating turbulence modelling by multi-agent reinforcement learning. 87-96 - Yong Wang
Author Correction: Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning. 97 - Guido Novati
Publisher Correction: Automating turbulence modelling by multi-agent reinforcement learning. 98 - Guido Novati
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
Institutionalizing ethics in AI through broader impact requirements. 104-110 - Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo
A definition, benchmark and database of AI for social good initiatives. 111-115 - Daniel Ahmed
Bioinspired acousto-magnetic microswarm robots with upstream motility. 116-124 - Rens van de Schoot
An open source machine learning framework for efficient and transparent systematic reviews. 125-133 - Deepak Baby
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications. 134-143 - Philippe Schwaller
Mapping the space of chemical reactions using attention-based neural networks. 144-152 - Shigeyuki Matsumoto
Extraction of protein dynamics information from cryo-EM maps using deep learning. 153-160 - Zhengyang Wang
Global voxel transformer networks for augmented microscopy. 161-171 - An Zheng
Deep neural networks identify sequence context features predictive of transcription factor binding. 172-180 - Siyuan Liu
Publisher Correction: Learning MRI artefact removal with unpaired data. 181 - Itai Orr
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
Physical human-robot interaction for clinical care in infectious environments. 184-186 - Vidushi Marda
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
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
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
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. 218-229 - Christoph Stöckl
Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes. 230-238 - Itai Orr
High-resolution radar road segmentation using weakly supervised learning. 239-246 - Thai-Hoang Pham
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
Improving representations of genomic sequence motifs in convolutional networks with exponential activations. 258-266 - Yoseob Han
Deep learning STEM-EDX tomography of nanocrystals. 267-274
Volume 3, Number 4, April 2021
- People have the AI power. 275
- Ross D. King
Cross-validation is safe to use. 276 - Alejandro A. Franco
Escape from flatland. 277-278 - Rohit Bhargava
Enhancing hyperspectral imaging. 279-280 - Daniel J. Gauthier
Predicting hidden structure in dynamical systems. 281-282 - Boris Babic, Sara Gerke
Direct-to-consumer medical machine learning and artificial intelligence applications. 283-287 - Edward Korot
Code-free deep learning for multi-modality medical image classification. 288-298 - Steve Kench
Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. 299-305 - Bryce Manifold
A versatile deep learning architecture for classification and label-free prediction of hyperspectral images. 306-315 - Jason Z. Kim
Teaching recurrent neural networks to infer global temporal structure from local examples. 316-323 - Donatas Repecka
Expanding functional protein sequence spaces using generative adversarial networks. 324-333 - Wan Xiang Shen
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations. 334-343 - Lauri Salmela
Predicting ultrafast nonlinear dynamics in fibre optics with a recurrent neural network. 344-354 - Alexander Binder
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
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
Neural network architecture search with AMBER. 372-373 - Shangying Wang
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
Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network. 387-391 - Zijun Zhang
An automated framework for efficiently designing deep convolutional neural networks in genomics. 392-400 - Harsh Bhatia
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations. 401-409 - Tønnes F. Nygaard
Real-world embodied AI through a morphologically adaptive quadruped robot. 410-419 - Rui Qiao
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
Dimensionality reduction to maximize prediction generalization capability. 434-446 - Darius Roman
Machine learning pipeline for battery state-of-health estimation. 447-456 - Takuya Isomura
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
Large language models associate Muslims with violence. 461-463 - Ania Korsunska
Back to the future with machine learning. 464-465 - Silvia Milano
Epistemic fragmentation poses a threat to the governance of online targeting. 466-472 - Georgios Kaissis
End-to-end privacy preserving deep learning on multi-institutional medical imaging. 473-484 - Alessandra Toniato
Unassisted noise reduction of chemical reaction datasets. 485-494 - Biagio Brattoli, Uta Büchler
Unsupervised behaviour analysis and magnification (uBAM) using deep learning. 495-506 - Xiaoyan Yin
Integration of deep learning and soft robotics for a biomimetic approach to nonlinear sensing. 507-512 - Roman Schulte-Sasse
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. 513-526 - Govinda B. Kc
A machine learning platform to estimate anti-SARS-CoV-2 activities. 527-535 - Qiao Liu
Simultaneous deep generative modelling and clustering of single-cell genomic data. 536-544 - Enrica Soria
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
Governing AI safety through independent audits. 566-571 - Jon Paul Janet
Reusability report: Learning the language of synthetic methods used in medicinal chemistry. 572-575 - Yijun Bao
Segmentation of neurons from fluorescence calcium recordings beyond real time. 590-600 - Jinbo Xu
Improved protein structure prediction by deep learning irrespective of co-evolution information. 601-609 - Alex J. DeGrave
AI for radiographic COVID-19 detection selects shortcuts over signal. 610-619 - Gabriel G. Erion
Improving performance of deep learning models with axiomatic attribution priors and expected gradients. 620-631 - Brodie Fischbacher
Modular deep learning enables automated identification of monoclonal cell lines. 632-640 - Christian Lagemann
Deep recurrent optical flow learning for particle image velocimetry data. 641-651 - Ania Korsunska
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
Machine learning and algorithmic fairness in public and population health. 659-666 - Christopher Irrgang
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
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors. 675-686 - Martin Bichler
Learning equilibria in symmetric auction games using artificial neural networks. 687-695 - James Lu
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modelling. 696-704 - Lukas M. Simon
Integration of millions of transcriptomes using batch-aware triplet neural networks. 705-715 - Ahmed M. Alaa
Machine learning to guide the use of adjuvant therapies for breast cancer. 716-726 - Marie Weiel
Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions. 727-734 - Jungkyu Park
Lessons from the first DBTex Challenge. 735-736
Volume 3, Number 9, September 2021
- The big question. 737
- Kerstin N. Vokinger
Regulating AI in medicine in the United States and Europe. 738-739 - Nabil Imam
Wiring up recurrent neural networks. 740-741 - Charlotte Frenkel
Sparsity provides a competitive advantage. 742-743 - Marcello Ienca
Ethical requirements for responsible research with hacked data. 744-748 - Dmitrii Usynin
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. 749-758 - Michael A. Skinnider
Chemical language models enable navigation in sparsely populated chemical space. 759-770 - Laura E. Suárez
Learning function from structure in neuromorphic networks. 771-786 - Jia Wu
Radiological tumour classification across imaging modality and histology. 787-798 - Alvaro Gomariz
Modality attention and sampling enables deep learning with heterogeneous marker combinations in fluorescence microscopy. 799-811 - Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra
Automating crystal-structure phase mapping by combining deep learning with constraint reasoning. 812-822 - Julian Göltz
Fast and energy-efficient neuromorphic deep learning with first-spike times. 823-835
Volume 3, Number 10, October 2021
- Robotic body augmentation. 837
- Yue Wang
Machine intelligence enabled radiomics. 838-839 - Nicholas A. Lesica
Harnessing the power of artificial intelligence to transform hearing healthcare and research. 840-849 - Giulia Dominijanni, Solaiman Shokur
The neural resource allocation problem when enhancing human bodies with extra robotic limbs. 850-860 - Hyungjin Chung
Reusability report: Feature disentanglement in generating a three-dimensional structure from a two-dimensional slice with sliceGAN. 861-863 - Tianshi Lu, Ze Zhang, James Zhu, Yunguan Wang
Deep learning-based prediction of the T cell receptor-antigen binding specificity. 864-875 - Darui Jin
Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning. 876-884 - Zhongqi Miao
Iterative human and automated identification of wildlife images. 885-895 - Kit T. Rodolfa
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy. 896-904 - Bojian Yin
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. 905-913 - Jike Wang, Chang-Yu Hsieh, Mingyang Wang, Xiaorui Wang, Zhenxing Wu, Dejun Jiang, Benben Liao, Xujun Zhang, Bo Yang, Qiaojun He, Dongsheng Cao
Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning. 914-922 - Christophe De Wagter
Learning fast in autonomous drone racing. 923
Volume 3, Number 11, November 2021
- Optimizing the synergy between physics and machine learning. 925
- Ricardo Vinuesa
Interpretable deep-learning models to help achieve the Sustainable Development Goals. 926 - Michele Colledanchise
Address behaviour vulnerabilities in the next generation of autonomous robots. 927-928 - Rohan Shad
Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging. 929-935 - Milena Pavlovic
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. 936-944 - Kai Fukami
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning. 945-951 - Mohamed Hibat-Allah
Variational neural annealing. 952-961 - Hossein Sharifi-Noghabi
Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction. 962-972 - Michael A. Skinnider
A deep generative model enables automated structure elucidation of novel psychoactive substances. 973-984 - Dongmyung Shin
Deep reinforcement learning-designed radiofrequency waveform in MRI. 985-994 - Jing Gong, Kui Xu
A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments. 995-1006
Volume 3, Number 12, December 2021
- A new age for content filters. 1007
- Simon Mayer
Improving customer decisions in web-based e-commerce through guerrilla modding. 1008-1010 - Yingying Zhang
Combining views for newly sequenced organisms. 1011-1012 - Pat Pataranutaporn, Valdemar Danry, Joanne Leong, Parinya Punpongsanon
AI-generated characters for supporting personalized learning and well-being. 1013-1022 - Kenneth Atz
Geometric deep learning on molecular representations. 1023-1032 - Oscar Méndez-Lucio
A geometric deep learning approach to predict binding conformations of bioactive molecules. 1033-1039 - Ziqi Chen
A deep generative model for molecule optimization via one fragment modification. 1040-1049 - Mateo Torres
Protein function prediction for newly sequenced organisms. 1050-1060 - Alina Jade Barnett
A case-based interpretable deep learning model for classification of mass lesions in digital mammography. 1061-1070 - Arif Ahmed Sekh
Physics-based machine learning for subcellular segmentation in living cells. 1071-1080 - Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan
Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence. 1081-1089
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