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

Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications

Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2025) · AI and Ethics · doi:10.1007/s43681-023-00331-3 · Cited by 68

Accelerometry-Based Home Monitoring for Detection of Nocturnal Hypermotor Seizures Based on Novelty Detection

Cuppens, K., Karsmakers, P., Van de Vel, A., Bonroy, B., Milosevic, M., Luca, S., Croonenborghs, T., Ceulemans, B., Lagae, L., Van Huffel, S., et al. (2014) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2285015 · Cited by 66

Fadl Federated-autonomous deep learning for distributed electronic health record

Liu, D., Miller, T., Sayeed, R., & Mandl, K. D. (2018) · arXiv · doi:10.48550/arxiv.1811.11400 · Cited by 65

Bayesian mechanics for stationary processes

Da Costa, L., Friston, K., Heins, C., & Pavliotis, G. A. (2021) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.2021.0518 · Cited by 63

Comparison of FDA-based and PCA-based features in fault diagnosis of automobile gearboxes

Gharavian, M. H., Almas Ganj, F., Ohadi, A. R., & Heidari Bafroui, H. (2013) · Neurocomputing · doi:10.1016/j.neucom.2013.04.033 · Cited by 62

Bayesian multi-task learning for decoding multi-subject neuroimaging data

Marquand, A. F., Brammer, M., Williams, S. C. R., & Doyle, O. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.02.008 · Cited by 59

Automatic Voice Pathology Detection With Running Speech by Using Estimation of Auditory Spectrum and Cepstral Coefficients Based on the All-Pole Model

Ali, Z., Elamvazuthi, I., Alsulaiman, M., & Muhammad, G. (2016) · Journal of Voice · doi:10.1016/j.jvoice.2015.08.010 · Cited by 59

Machine learning-guided search for high-efficiency perovskite solar cells with doped electron transport layers

She, C., Huang, Q., Chen, C., Jiang, Y., Fan, Z., & Gao, J. (2021) · Journal of Materials Chemistry A · doi:10.1039/d1ta08194b · Cited by 59

Machine Learning Approaches for Early DRG Classification and Resource Allocation

Gartner, D., Kolisch, R., Neill, D. B., & Padman, R. (2015) · INFORMS Journal on Computing · doi:10.1287/ijoc.2015.0655 · Cited by 58

Predicting phenotypes from genetic, environment, management, and historical data using CNNs

Washburn, J. D., Cimen, E., Ramstein, G., Reeves, T., O’Briant, P., McLean, G., Cooper, M., Hammer, G., & Buckler, E. S. (2021) · Theoretical and Applied Genetics · doi:10.1007/s00122-021-03943-7 · Cited by 57

A co-adaptive brain-computer interface for end users with severe motor impairment

Faller, J., Scherer, R., Costa, U., Opisso, E., Medina, J., & Müller-Putz, G. R. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101168 · Cited by 55

Oxytocin receptor gene variations predict neural and behavioral response to oxytocin in autism

Watanabe, T., Otowa, T., Abe, O., Kuwabara, H., Aoki, Y., Natsubori, T., Takao, H., Kakiuchi, C., Kondo, K., Ikeda, M., et al. (2017) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw150 · Cited by 54

A hybrid SLAM representation for dynamic marine environments

Bibby, C., & Reid, I. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509262 · Cited by 53

Sequential Feature Explanations for Anomaly Detection

Siddiqui, M. A., Fern, A., Dietterich, T. G., & Wong, W.-K. (2019) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3230666 · Cited by 50

Deep Big Multilayer Perceptrons for Digit Recognition

Cireşan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_31 · Cited by 48

Smartphone-based real time vehicle tracking in indoor parking structures

Gao, R., Zhao, M., Ye, T., Ye, F., Wang, Y., & Luo, G. (2017) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2017.2684167 · Cited by 48

Combining macula clinical signs and patient characteristics for age-related macular degeneration diagnosis: a machine learning approach

Fraccaro, P., Nicolo, M., Bonetto, M., Giacomini, M., Weller, P., Traverso, C. E., Prosperi, M., & OSullivan, D. (2015) · BMC Ophthalmology · doi:10.1186/1471-2415-15-10 · Cited by 48

Detecting anomalies in dynamic rating data: a robust probabilistic model for rating evolution

Günnemann, S., Günnemann, N., & Faloutsos, C. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623721 · Cited by 47

Data Driven Approach to Failure Prediction for Electrical Submersible Pump Systems

Guo, D., Raghavendra, C. S., Yao, K.-T., Harding, M., Anvar, A., & Patel, A. (2015) · SPE Western Regional Meeting · doi:10.2118/174062-ms · Cited by 49

Acceleration of FPGA based convolutional neural network for human activity classification using millimeter-wave radar

Lei, P., Liang, J., Guan, Z., Wang, J., & Zheng, T. (2019) · IEEE Access · doi:10.1109/access.2019.2926381 · Cited by 44

Mobile Traffic Classification Through Physical Control Channel Fingerprinting: A Deep Learning Approach

Trinh, H. D., Fernandez Gambin, A., Giupponi, L., Rossi, M., & Dini, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2020.3028197 · Cited by 40

Machine learning for predicting levetiracetam treatment response in temporal lobe epilepsy

Croce, P., Ricci, L., Pulitano, P., Boscarino, M., Zappasodi, F., Lanzone, J., Narducci, F., Mecarelli, O., Di Lazzaro, V., Tombini, M., et al. (2021) · Clinical Neurophysiology · doi:10.1016/j.clinph.2021.08.024 · Cited by 38

Investigating the relationship between emotional granularity and cardiorespiratory physiological activity in daily life

Hoemann, K., Khan, Z., Kamona, N., Dy, J., Barrett, L. F., & Quigley, K. S. (2021) · Psychophysiology · doi:10.1111/psyp.13818 · Cited by 40

Machine learning for toxicity characterization of organic chemical emissions using USEtox database: Learning the structure of the input space

Marvuglia, A., Kanevski, M., & Benetto, E. (2015) · Environment International · doi:10.1016/j.envint.2015.05.011 · Cited by 41

Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: the town of Zielona Góra

Skiba, M., Mrówczyńska, M., & Bazan-Krzywoszańska, A. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.006 · Cited by 40

Laplace approximation in sparse Bayesian learning for structural damage detection

Wang, X., Hou, R., Xia, Y., & Zhou, X. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.106701 · Cited by 32

A simple approach to ignoring irrelevant variables by population decoding based on multisensory neurons

Kim, H. R., Pitkow, X., Angelaki, D. E., & DeAngelis, G. C. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00005.2016 · Cited by 32

Predicting winning and losing businesses when changing electricity tariffs

Granell, R., Axon, C. J., & Wallom, D. C. H. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.07.098 · Cited by 30

Multimodal modeling of collaborative problem-solving facets in triads

Stewart, A. E. B., Keirn, Z., & D’Mello, S. K. (2021) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-021-09290-y · Cited by 30

Hospital Admission Location Prediction via Deep Interpretable Networks for the Year-Round Improvement of Emergency Patient Care

El-Bouri, R., Eyre, D. W., Watkinson, P., Zhu, T., & Clifton, D. A. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2990309 · Cited by 30

A hypergraph based Kohonen map for detecting intrusions over cyber–physical systems traffic

Jagtap, S. S., V. S., S. S., & V., S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.02.001 · Cited by 29

Cognitive Network Inference through Bayesian Network Analysis

Quer, G., Meenakshisundaram, H., Tamma, B., Manoj, B. S., Rao, R., & Zorzi, M. (2010) · 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 · doi:10.1109/glocom.2010.5683282 · Cited by 29

Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease

Andrés-Blanco, A. M., Álvarez, D., Crespo, A., Arroyo, C. A., Cerezo-Hernández, A., Gutiérrez-Tobal, G. C., Hornero, R., & del Campo, F. (2017) · PLOS ONE · doi:10.1371/journal.pone.0188094 · Cited by 28

A review of computational intelligence techniques in coral reef-related applications

Salcedo-Sanz, S., Cuadra, L., & Vermeij, M. J. A. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.01.008 · Cited by 27

Detection of Cyber Grooming in Online Conversation

Bours, P., & Kulsrud, H. (2019) · 2019 IEEE International Workshop on Information Forensics and Security (WIFS) · doi:10.1109/wifs47025.2019.9035090 · Cited by 27

Lineage marker synchrony in hematopoietic genealogies refutes the PU.1/GATA1 toggle switch paradigm

Strasser, M. K., Hoppe, P. S., Loeffler, D., Kokkaliaris, K. D., Schroeder, T., Theis, F. J., & Marr, C. (2018) · Nature Communications · doi:10.1038/s41467-018-05037-3 · Cited by 26

Learning to Play Minigolf: A Dynamical System-Based Approach

Khansari-Zadeh, S. M., Kronander, K., & Billard, A. (2012) · Advanced Robotics · doi:10.1080/01691864.2012.728692 · Cited by 28

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

Buettner, F., Moignard, V., Göttgens, B., & Theis, F. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu134 · Cited by 21

Distribution System Topology Identification for DER Management Systems Using Deep Neural Networks

Jafarian, M., Soroudi, A., & Keane, A. (2020) · 2020 IEEE Power & Energy Society General Meeting (PESGM) · doi:10.1109/pesgm41954.2020.9282121 · Cited by 25

Bayesian active probabilistic classification for psychometric field estimation

Song, X. D., Sukesan, K. A., & Barbour, D. L. (2018) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-017-1460-0 · Cited by 22

Predicting improved protein conformations with a temporal deep recurrent neural network

Pfeiffenberger, E., & Bates, P. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0202652 · Cited by 21

Tracking single units in chronic, large scale, neural recordings for brain machine interface applications

Eleryan, A., Vaidya, M., Southerland, J., Badreldin, I. S., Balasubramanian, K., Fagg, A. H., Hatsopoulos, N., & Oweiss, K. (2014) · Frontiers in Neuroengineering · doi:10.3389/fneng.2014.00023 · Cited by 20

Probabilistic inference of Bayesian neural networks with generalized expectation propagation

Zhao, J., Liu, X., He, S., & Sun, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.06.060 · Cited by 20

A neural circuit architecture for rapid behavioral flexibility in goal-directed navigation

Dan, C., Hulse, B. K., Kappagantula, R., Jayaraman, V., & Hermundstad, A. M. (2021) · doi:10.1101/2021.08.18.456004 · Cited by 51

A novel sequence alignment algorithm based on deep learning of the protein folding code

Gao, M., & Skolnick, J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa810 · Cited by 18

On universal common ancestry, sequence similarity, and phylogenetic structure: the sins of P-values and the virtues of Bayesian evidence

Theobald, D. L. (2011) · Biology Direct · doi:10.1186/1745-6150-6-60 · Cited by 16

Model Refinement Learning and an Example on Channel Estimation With Universal Noise Model

Wang, H.-M., Jiang, J.-C., & Wang, Y.-N. (2021) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2020.3036964 · Cited by 14

Computational Models of Hallucinations

Jardri, R., & Denève, S. (2013) · The Neuroscience of Hallucinations · doi:10.1007/978-1-4614-4121-2_16 · Cited by 16

Learning to (Learn at Test Time): RNNs with Expressive Hidden States

Sun, Y., Li, X., Dalal, K., Xu, J., Vikram, A., Zhang, G., Dubois, Y., Chen, X., Wang, X., Koyejo, S., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.04620 · Cited by 18

Visual-based classification models for grading reclaimed structural timber for reuse: A theoretical, numerical and experimental investigation

Pasca, D. P., Aloisio, A., De Santis, Y., Burkart, H., & Øvrum, A. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2024.119218 · Cited by 15

Bidirectional invariant representation of rigid body motions and its application to gesture recognition and reproduction

Lee, D., Soloperto, R., & Saveriano, M. (2018) · Autonomous Robots · doi:10.1007/s10514-017-9645-x · Cited by 11

Quantum computing and supervised machine learning

Oneto, L., Ridella, S., & Anguita, D. (2017) · Quantum Inspired Computational Intelligence · doi:10.1016/b978-0-12-804409-4.00002-4 · Cited by 11

Workflow extraction for service operation using multiple unstructured trouble tickets

Watanabe, A., Ishibashi, K., Toyono, T., Kimura, T., Watanabe, K., Matsuo, Y., & Shiomoto, K. (2016) · NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium · doi:10.1109/noms.2016.7502872 · Cited by 11

Calibration of Model Uncertainty for Dropout Variational Inference

Laves, M.-H., Ihler, S., Kortmann, K.-P., & Ortmaier, T. (2020) · arXiv · doi:10.48550/arxiv.2006.11584 · Cited by 11

A Novel Machine Learning Based Framework for Detection of Autism Spectrum Disorder (ASD)

Sharif, H., & Khan, R. A. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2004655 · Cited by 10

Cognitive Call Admission Control for VoIP over IEEE 802.11 Using Bayesian Networks

Quer, G., Baldo, N., & Zorzi, M. (2011) · 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011 · doi:10.1109/glocom.2011.6133955 · Cited by 10

A dynamic Bayesian model of homeostatic control

Penny, W., & Stephan, K. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11298-5_7 · Cited by 10

A probabilistic gene expression barcode for annotation of cell-types from single cell RNA-seq data

Grabski, I. N., & Irizarry, R. A. (2020) · doi:10.1101/2020.01.05.895441 · Cited by 7

The Dual Form of Neural Networks Revisited: Connecting Test Time Predictions to Training Patterns via Spotlights of Attention

Irie, K., Csordás, R., & Schmidhuber, J. (2022) · arXiv · doi:10.48550/arxiv.2202.05798 · Cited by 6

İLERİ-GERİ TAKİP ALGORİTMASI TABANLI SEYREK AŞIRI ÖĞRENME MAKİNESİ

ALÇİN, Ö. F., ŞENGÜR, A., & İNCE, M. C. (2015) · Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi · doi:10.17341/gummfd.91022 · Cited by 2

Evaluation of LLM Reasoning Under Uncertainty: An Atomic Comparison to Normative Approaches

de Villiers, J. P., de Freitas, A., Jousselme, A.-L., Kaplan, L., Blasch, E., Laudy, C., & Costa, P. C. (2025) · 2025 28th International Conference on Information Fusion (FUSION) · doi:10.23919/fusion65864.2025.11123952 · Cited by 1

Temporal pattern classification of internet meme propagation: A hybrid machine learning approach

Little, W., Department of Mathematics, K. S. U., Xiao, P., & Department of Computer Science, K. S. U. (2025) · Electronic Research Archive · doi:10.3934/era.2025238

CROP: Token-Efficient Reasoning in Large Language Models via Regularized Prompt Optimization

Shah, D., Badhe, S., Kathrotia, N., & Tiwari, P. (2026) · arXiv