Book Review: Pattern Recognition and Machine Learning

Bayesian Data Analysis

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276

Fully convolutional networks for semantic segmentation

Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

Making a “Completely Blind” Image Quality Analyzer

Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766

Analyzing and Improving the Image Quality of StyleGAN

Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Grimmer, J., & Stewart, B. M. (2013) · Political Analysis · doi:10.1093/pan/mps028 · Cited by 3,297

Deep learning for time series classification: a review

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2019) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-019-00619-1 · Cited by 3,342

Machine Learning Applications in Cancer Prognosis and Prediction

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239

Supervised Sequence Labelling with Recurrent Neural Networks

Graves, A. (2012) · Studies in Computational Intelligence · doi:10.1007/978-3-642-24797-2 · Cited by 3,120

Reinforcement learning in robotics: A survey

Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160

Circular analysis in systems neuroscience: the dangers of double dipping

Kriegeskorte, N., Simmons, W. K., Bellgowan, P. S. F., & Baker, C. I. (2009) · Nature Neuroscience · doi:10.1038/nn.2303 · Cited by 2,696

A survey on semi-supervised learning

van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update

Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239

Probabilistic machine learning and artificial intelligence

Ghahramani, Z. (2015) · Nature · doi:10.1038/nature14541 · Cited by 1,995

Quantum Support Vector Machine for Big Data Classification

Rebentrost, P., Mohseni, M., & Lloyd, S. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.130503 · Cited by 1,951

Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation

Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890

Homo Heuristicus: Why Biased Minds Make Better Inferences

Gigerenzer, G., & Brighton, H. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2008.01006.x · Cited by 1,801

The Balanced Accuracy and Its Posterior Distribution

Brodersen, K. H., Ong, C. S., Stephan, K. E., & Buhmann, J. M. (2010) · 2010 20th International Conference on Pattern Recognition · doi:10.1109/icpr.2010.764 · Cited by 1,816

A review on lithium-ion battery ageing mechanisms and estimations for automotive applications

Barré, A., Deguilhem, B., Grolleau, S., Gérard, M., Suard, F., & Riu, D. (2013) · Journal of Power Sources · doi:10.1016/j.jpowsour.2013.05.040 · Cited by 1,758

A review of novelty detection

Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649

Aggregating Local Image Descriptors into Compact Codes

Jegou, H., Perronnin, F., Douze, M., Sanchez, J., Perez, P., & Schmid, C. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.235 · Cited by 1,503

Bayesian model selection for group studies

Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., & Friston, K. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.03.025 · Cited by 1,423

Lost in quantization: Improving particular object retrieval in large scale image databases

Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587635 · Cited by 1,478

Generative AI

Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 2,267

Quantum principal component analysis

Lloyd, S., Mohseni, M., & Rebentrost, P. (2014) · Nature Physics · doi:10.1038/nphys3029 · Cited by 1,385

A deep cascade of convolutional neural networks for dynamic MR image reconstruction

Schlemper, J., Caballero, J., Hajnal, J. V., Price, A. N., & Rueckert, D. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2760978 · Cited by 1,338

Machine learning in manufacturing: Advantages, challenges, and applications

Wuest, T., Weimer, D., Irgens, C., & Thoben, K.-D. (2016) · Production & Manufacturing Research · doi:10.1080/21693277.2016.1192517 · Cited by 1,319

Materials discovery and design using machine learning

Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224

Adversarial machine learning

Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I. P., & Tygar, J. D. (2011) · Proceedings of the 4th ACM workshop on Security and artificial intelligence · doi:10.1145/2046684.2046692 · Cited by 1,194

Emerging Artificial Intelligence Methods in Structural Engineering

Salehi, H., & Burgueño, R. (2018) · Engineering Structures · doi:10.1016/j.engstruct.2018.05.084 · Cited by 1,068

Multi-column deep neural network for traffic sign classification

Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017) · IEEE Access · doi:10.1109/access.2017.2756872 · Cited by 922

An Overview of Artificial Intelligence Applications for Power Electronics

Zhao, S., Blaabjerg, F., & Wang, H. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2020.3024914 · Cited by 947

An efficient P300-based brain–computer interface for disabled subjects

Hoffmann, U., Vesin, J.-M., Ebrahimi, T., & Diserens, K. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.005 · Cited by 859

What Are Artificial Neural Networks?

Krogh, A. (2008) · Nature Biotechnology · doi:10.1038/nbt1386 · Cited by 844

Scalable Quantum Simulation of Molecular Energies

O’Malley, P. J. J., Babbush, R., Kivlichan, I. D., Romero, J., McClean, J. R., Barends, R., Kelly, J., Roushan, P., Tranter, A., Ding, N., et al. (2016) · Physical Review X · doi:10.1103/physrevx.6.031007 · Cited by 1,059

Systematic dissection and optimization of inducible enhancers in human cells using a massively parallel reporter assay

Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T., Wang, L., Rogov, P., Feizi, S., Gnirke, A., Callan, C. G., Kinney, J. B., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2137 · Cited by 711

A review on prognostics and health monitoring of Li-ion battery

Zhang, J., & Lee, J. (2011) · Journal of Power Sources · doi:10.1016/j.jpowsour.2011.03.101 · Cited by 792

Consumer credit-risk models via machine-learning algorithms

Khandani, A. E., Kim, A. J., & Lo, A. W. (2010) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2010.06.001 · Cited by 864

Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Zeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012) · Brain · doi:10.1093/brain/aws059 · Cited by 748

Adversarially Learned One-Class Classifier for Novelty Detection

Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765

Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies

Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683

Hey Alexa . . . examine the variables influencing the use of artificial intelligent in-home voice assistants

McLean, G., & Osei-Frimpong, K. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.05.009 · Cited by 762

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

Lu, S., Zhou, Q., Ouyang, Y., Guo, Y., Li, Q., & Wang, J. (2018) · Nature Communications · doi:10.1038/s41467-018-05761-w · Cited by 725

Training deep quantum neural networks

Beer, K., Bondarenko, D., Farrelly, T., Osborne, T. J., Salzmann, R., Scheiermann, D., & Wolf, R. (2020) · Nature Communications · doi:10.1038/s41467-020-14454-2 · Cited by 726

Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions

Rodríguez-Pérez, R., & Bajorath, J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00314-0 · Cited by 718

The Tolman-Eichenbaum machine: unifying space and relational memory through generalization in the hippocampal formation

Whittington, J. C. R., Muller, T. H., Mark, S., Chen, G., Barry, C., Burgess, N., & Behrens, T. E. J. (2020) · Cell · doi:10.1016/j.cell.2020.10.024 · Cited by 660

Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

Marshall, I. J., & Wallace, B. C. (2019) · Systematic Reviews · doi:10.1186/s13643-019-1074-9 · Cited by 638

Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination

Datta, A., Tschantz, M. C., & Datta, A. (2015) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2015-0007 · Cited by 676

PUF Modeling Attacks on Simulated and Silicon Data

Ruhrmair, U., Solter, J., Sehnke, F., Xu, X., Mahmoud, A., Stoyanova, V., Dror, G., Schmidhuber, J., Burleson, W., & Devadas, S. (2013) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2013.2279798 · Cited by 637

Psychological entropy: a framework for understanding uncertainty-related anxiety

Hirsh, J. B., Mar, R. A., & Peterson, J. B. (2012) · Psychological Review · doi:10.1037/a0026767 · Cited by 606

Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches

Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2954595 · Cited by 620

Active learning

Settles, B. (2012) · Synthesis Lectures on Artificial Intelligence and Machine Learning · doi:10.2200/s00429ed1v01y201207aim018 · Cited by 605

The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions

Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3077 · Cited by 510

Doing Bayesian Data Analysis: A Tutorial with R and BUGS

Barry, J. (2011) · Europe’s Journal of Psychology · doi:10.5964/ejop.v7i4.163 · Cited by 563

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

Jones, M., & Love, B. C. (2011) · Behavioral and Brain Sciences · doi:10.1017/s0140525x10003134 · Cited by 533

A Very Brief Introduction to Machine Learning With Applications to Communication Systems

Simeone, O. (2018) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2018.2881442 · Cited by 516

Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis

Alwosheel, A., van Cranenburgh, S., & Chorus, C. G. (2018) · Journal of Choice Modelling · doi:10.1016/j.jocm.2018.07.002 · Cited by 524

Uncertainty in perception and the Hierarchical Gaussian Filter

Mathys, C. D., Lomakina, E. I., Daunizeau, J., Iglesias, S., Brodersen, K. H., Friston, K. J., & Stephan, K. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00825 · Cited by 465

Transfer Learning for Brain–Computer Interfaces: A Euclidean Space Data Alignment Approach

He, H., & Wu, D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2019.2913914 · Cited by 569

Automatic differentiation variational inference

Kucukelbir, A., Tran, D., Ranganath, R., Gelman, A., & Blei, D. M. (2016) · arXiv · doi:10.48550/arxiv.1603.00788 · Cited by 515

Applications of machine learning in animal behaviour studies

Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505

Prescriptive analytics: Literature review and research challenges

Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501

Priors in whole-genome regression: the Bayesian alphabet returns

Gianola, D. (2013) · Genetics · doi:10.1534/genetics.113.151753 · Cited by 457

Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Ramgopal, S., Thome-Souza, S., Jackson, M., Kadish, N. E., Sánchez Fernández, I., Klehm, J., Bosl, W., Reinsberger, C., Schachter, S., & Loddenkemper, T. (2014) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2014.06.023 · Cited by 469

Methodologies for Cross-Domain Data Fusion: An Overview

Zheng, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2465959 · Cited by 454

Using control genes to correct for unwanted variation in microarray data

Gagnon-Bartsch, J. A., & Speed, T. P. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr034 · Cited by 442

A comparative study of machine learning classifiers for modeling travel mode choice

Hagenauer, J., & Helbich, M. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.01.057 · Cited by 424

Spotting opinion spammers using behavioral footprints

Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487580 · Cited by 426

BCILAB: a platform for brain–computer interface development

Kothe, C. A., & Makeig, S. (2013) · Journal of Neural Engineering · doi:10.1088/1741-2560/10/5/056014 · Cited by 412

Beyond Atopy: Multiple Patterns of Sensitization in Relation to Asthma in a Birth Cohort Study

Simpson, A., Tan, V. Y. F., Winn, J., Svensén, M., Bishop, C. M., Heckerman, D. E., Buchan, I., & Custovic, A. (2010) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.200907-1101oc · Cited by 415

CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction

Sun, Y., Yin, X., Jiang, J., Sekar, V., Lin, F., Wang, N., Liu, T., & Sinopoli, B. (2016) · Proceedings of the 2016 ACM SIGCOMM Conference · doi:10.1145/2934872.2934898 · Cited by 407

Causal inference in perception

Shams, L., & Beierholm, U. R. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.07.001 · Cited by 403

Mood as representation of momentum

Eldar, E., Rutledge, R. B., Dolan, R. J., & Niv, Y. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.07.010 · Cited by 383

Novelty or Surprise?

Barto, A., Mirolli, M., & Baldassarre, G. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00907 · Cited by 397

Robust Network Traffic Classification

Zhang, J., Chen, X., Xiang, Y., Zhou, W., & Wu, J. (2015) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2014.2320577 · Cited by 391

Automatic speech emotion recognition using modulation spectral features

Wu, S., Falk, T. H., & Chan, W.-Y. (2011) · Speech Communication · doi:10.1016/j.specom.2010.08.013 · Cited by 389

Data mining and machine learning in astronomy

BALL, N. M., & BRUNNER, R. J. (2010) · International Journal of Modern Physics D · doi:10.1142/s0218271810017160 · Cited by 383

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons

Buesing, L., Bill, J., Nessler, B., & Maass, W. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002211 · Cited by 384

Network anomaly detection with the restricted Boltzmann machine

Fiore, U., Palmieri, F., Castiglione, A., & De Santis, A. (2013) · Neurocomputing · doi:10.1016/j.neucom.2012.11.050 · Cited by 376

Automated monitoring and analysis of social behavior in Drosophila

Dankert, H., Wang, L., Hoopfer, E. D., Anderson, D. J., & Perona, P. (2009) · Nature Methods · doi:10.1038/nmeth.1310 · Cited by 382

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access · doi:10.1109/access.2020.3037715 · Cited by 368

Short-term emotion assessment in a recall paradigm

Chanel, G., Kierkels, J. J. M., Soleymani, M., & Pun, T. (2009) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2009.03.005 · Cited by 367

Rational approximations to rational models: alternative algorithms for category learning

Sanborn, A. N., Griffiths, T. L., & Navarro, D. J. (2010) · Psychological Review · doi:10.1037/a0020511 · Cited by 369

Predicting Protein–Protein Interactions from the Molecular to the Proteome Level

Keskin, O., Tuncbag, N., & Gursoy, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00683 · Cited by 369

Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials

Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344

The present and future of deep learning in radiology

Saba, L., Biswas, M., Kuppili, V., Cuadrado Godia, E., Suri, H. S., Edla, D. R., Omerzu, T., Laird, J. R., Khanna, N. N., Mavrogeni, S., et al. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.02.038 · Cited by 353

Forming beliefs: Why valence matters

Sharot, T., & Garrett, N. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2015.11.002 · Cited by 345

Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex

Murugan, M., Jang, H. J., Park, M., Miller, E. M., Cox, J., Taliaferro, J. P., Parker, N. F., Bhave, V., Hur, H., Liang, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.11.002 · Cited by 311

Predicting Individual Differences in Placebo Analgesia: Contributions of Brain Activity during Anticipation and Pain Experience

Wager, T. D., Atlas, L. Y., Leotti, L. A., & Rilling, J. K. (2011) · The Journal of Neuroscience · doi:10.1523/jneurosci.3420-10.2011 · Cited by 328

Design, implementation and validation of a novel open framework for agile development of mobile health applications

Banos, O., Villalonga, C., Garcia, R., Saez, A., Damas, M., Holgado-Terriza, J. A., Lee, S., Pomares, H., & Rojas, I. (2015) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-14-s2-s6 · Cited by 321

Does feature selection improve classification accuracy? Impact of sample size and feature selection on classification using anatomical magnetic resonance images

Chu, C., Hsu, A.-L., Chou, K.-H., Bandettini, P., & Lin, C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.066 · Cited by 319

Clinical applications of the functional connectome

Castellanos, F. X., Di Martino, A., Craddock, R. C., Mehta, A. D., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.083 · Cited by 312

Machine learning for renewable energy materials

Gu, G. H., Noh, J., Kim, I., & Jung, Y. (2019) · Journal of Materials Chemistry A · doi:10.1039/c9ta02356a · Cited by 311

A primer on learning in Bayesian networks for computational biology

Needham, C. J., Bradford, J. R., Bulpitt, A. J., & Westhead, D. R. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030129 · Cited by 311

BodyScope: a wearable acoustic sensor for activity recognition

Yatani, K., & Truong, K. N. (2012) · Proceedings of the 2012 ACM Conference on Ubiquitous Computing · doi:10.1145/2370216.2370269 · Cited by 310

Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

Dong, H., Jin, X., Lou, Y., & Wang, C. (2014) · Journal of Power Sources · doi:10.1016/j.jpowsour.2014.07.176 · Cited by 310