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LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436444 (2015). This survey summarizes key elements of deep learning and its development in speech recognition, computer vision and and natural language processing.

Article ADS CAS PubMed Google Scholar

de Regt, H. W. Understanding, values, and the aims of science. Phil. Sci. 87, 921932 (2020).

Article MathSciNet Google Scholar

Pickstone, J. V. Ways of Knowing: A New History of Science, Technology, and Medicine (Univ. Chicago Press, 2001).

Han, J. et al. Deep potential: a general representation of a many-body potential energy surface. Commun. Comput. Phys. 23, 629639 (2018). This paper introduced a deep neural network architecture that learns the potential energy surface of many-body systems while respecting the underlying symmetries of the system by incorporating group theory.

Akiyama, K. et al. First M87 Event Horizon Telescope results. IV. Imaging the central supermassive black hole. Astrophys. J. Lett. 875, L4 (2019).

Article ADS CAS Google Scholar

Wagner, A. Z. Constructions in combinatorics via neural networks. Preprint at https://arxiv.org/abs/2104.14516 (2021).

Coley, C. W. et al. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365, eaax1566 (2019).

Article CAS PubMed Google Scholar

Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://arxiv.org/abs/2108.07258 (2021).

Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600, 7074 (2021). This paper explores how AI can aid the development of pure mathematics by guiding mathematical intuition.

Article ADS CAS PubMed PubMed Central MATH Google Scholar

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583589 (2021).This study was the first to demonstrate the ability to predict protein folding structures using AI methods with a high degree of accuracy, achieving results that are at or near the experimental resolution. This accomplishment is particularly noteworthy, as predicting protein folding has been a grand challenge in the field of molecular biology for over 50 years.

Article ADS CAS PubMed PubMed Central Google Scholar

Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 180, 688702 (2020).

Article CAS PubMed PubMed Central Google Scholar

Bohacek, R. S., McMartin, C. & Guida, W. C. The art and practice of structure-based drug design: a molecular modeling perspective. Med. Res. Rev. 16, 350 (1996).

Article CAS PubMed Google Scholar

Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40, 932937 (2022).

Bellemare, M. G. et al. Autonomous navigation of stratospheric balloons using reinforcement learning. Nature 588, 7782 (2020). This paper describes a reinforcement-learning algorithm for navigating a super-pressure balloon in the stratosphere, making real-time decisions in the changing environment.

Article ADS CAS PubMed Google Scholar

Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 9598 (2019).

Article ADS CAS PubMed Google Scholar

Zhang, L. et al. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 120, 143001 (2018).

Article ADS CAS PubMed Google Scholar

Deiana, A. M. et al. Applications and techniques for fast machine learning in science. Front. Big Data 5, 787421 (2022).

Karagiorgi, G. et al. Machine learning in the search for new fundamental physics. Nat. Rev. Phys. 4, 399412 (2022).

Zhou, C. & Paffenroth, R. C. Anomaly detection with robust deep autoencoders. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 665674 (2017).

Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504507 (2006).

Article ADS MathSciNet CAS PubMed MATH Google Scholar

Kasieczka, G. et al. The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics. Rep. Prog. Phys. 84, 124201 (2021).

Article ADS CAS Google Scholar

Govorkova, E. et al. Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nat. Mach. Intell. 4, 154161 (2022).

Article Google Scholar

Chamberland, M. et al. Detecting microstructural deviations in individuals with deep diffusion MRI tractometry. Nat. Comput. Sci. 1, 598606 (2021).

Article PubMed PubMed Central Google Scholar

Rafique, M. et al. Delegated regressor, a robust approach for automated anomaly detection in the soil radon time series data. Sci. Rep. 10, 3004 (2020).

Article ADS CAS PubMed PubMed Central Google Scholar

Pastore, V. P. et al. Annotation-free learning of plankton for classification and anomaly detection. Sci. Rep. 10, 12142 (2020).

Article ADS CAS PubMed PubMed Central Google Scholar

Naul, B. et al. A recurrent neural network for classification of unevenly sampled variable stars. Nat. Astron. 2, 151155 (2018).

Article ADS Google Scholar

Lee, D.-H. et al. Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In ICML Workshop on Challenges in Representation Learning (2013).

Zhou, D. et al. Learning with local and global consistency. In Advances in Neural Information Processing Systems 16, 321328 (2003).

Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10, 221227 (2013).

Article CAS PubMed PubMed Central Google Scholar

Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16, 695698 (2019).

Article CAS PubMed PubMed Central Google Scholar

Tran, K. & Ulissi, Z. W. Active learning across intermetallics to guide discovery of electrocatalysts for CO2 reduction and H2 evolution. Nat. Catal. 1, 696703 (2018).

Article CAS Google Scholar

Jablonka, K. M. et al. Bias free multiobjective active learning for materials design and discovery. Nat. Commun. 12, 2312 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Roussel, R. et al. Turn-key constrained parameter space exploration for particle accelerators using Bayesian active learning. Nat. Commun. 12, 5612 (2021).

Article ADS CAS PubMed PubMed Central Google Scholar

Ratner, A. J. et al. Data programming: creating large training sets, quickly. In Advances in Neural Information Processing Systems 29, 35673575 (2016).

Ratner, A. et al. Snorkel: rapid training data creation with weak supervision. In International Conference on Very Large Data Bases 11, 269282 (2017). This paper presents a weakly-supervised AI system designed to annotate massive amounts of data using labeling functions.

Butter, A. et al. GANplifying event samples. SciPost Phys. 10, 139 (2021).

Article ADS Google Scholar

Brown, T. et al. Language models are few-shot learners. In Advances in Neural Information Processing Systems 33, 18771901 (2020).

Ramesh, A. et al. Zero-shot text-to-image generation. In International Conference on Machine Learning 139, 88218831 (2021).

Littman, M. L. Reinforcement learning improves behaviour from evaluative feedback. Nature 521, 445451 (2015).

Article ADS CAS PubMed Google Scholar

Cubuk, E. D. et al. Autoaugment: learning augmentation strategies from data. In IEEE Conference on Computer Vision and Pattern Recognition 113123 (2019).

Reed, C. J. et al. Selfaugment: automatic augmentation policies for self-supervised learning. In IEEE Conference on Computer Vision and Pattern Recognition 26742683 (2021).

ATLAS Collaboration et al. Deep generative models for fast photon shower simulation in ATLAS. Preprint at https://arxiv.org/abs/2210.06204 (2022).

Mahmood, F. et al. Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39, 32573267 (2019).

Article Google Scholar

Teixeira, B. et al. Generating synthetic X-ray images of a person from the surface geometry. In IEEE Conference on Computer Vision and Pattern Recognition 90599067 (2018).

Lee, D., Moon, W.-J. & Ye, J. C. Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks. Nat. Mach. Intell. 2, 3442 (2020).

Article Google Scholar

Kench, S. & Cooper, S. J. Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion. Nat. Mach. Intell. 3, 299305 (2021).

Article Google Scholar

Wan, C. & Jones, D. T. Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nat. Mach. Intell. 2, 540550 (2020).

Article Google Scholar

Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324333 (2021).

Article Google Scholar

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11, 166 (2020).

Article ADS CAS PubMed PubMed Central Google Scholar

Ghahramani, Z. Probabilistic machine learning and artificial intelligence. Nature 521, 452459 (2015).This survey provides an introduction to probabilistic machine learning, which involves the representation and manipulation of uncertainty in models and predictions, playing a central role in scientific data analysis.

Article ADS CAS PubMed Google Scholar

Cogan, J. et al. Jet-images: computer vision inspired techniques for jet tagging. J. High Energy Phys. 2015, 118 (2015).

Article Google Scholar

Zhao, W. et al. Sparse deconvolution improves the resolution of live-cell super-resolution fluorescence microscopy. Nat. Biotechnol. 40, 606617 (2022).

Article CAS PubMed Google Scholar

Brbi, M. et al. MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat. Methods 17, 12001206 (2020).

Article PubMed Google Scholar

Qiao, C. et al. Evaluation and development of deep neural networks for image super-resolution in optical microscopy. Nat. Methods 18, 194202 (2021).

Article CAS PubMed Google Scholar

Andreassen, A. et al. OmniFold: a method to simultaneously unfold all observables. Phys. Rev. Lett. 124, 182001 (2020).

Article ADS CAS PubMed Google Scholar

Bergenstrhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476479 (2021).

Vincent, P. et al. Extracting and composing robust features with denoising autoencoders. In International Conference on Machine Learning 10961103 (2008).

Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. In International Conference on Learning Representations (2014).

Eraslan, G. et al. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).

Article ADS CAS PubMed PubMed Central Google Scholar

Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016).

Olshausen, B. A. & Field, D. J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607609 (1996).

Article ADS CAS PubMed Google Scholar

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