On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means

Generative Adversarial Nets

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Symmetrizing the Kullback-Leibler Distance

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An Elementary Introduction to Information Geometry

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Low-Rank Kernel Learning with Bregman Matrix Divergences

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Properties of classical and quantum Jensen-Shannon divergence

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A new class of metric divergences on probability spaces and its applicability in statistics

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Generalised Thermostatistics

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Jensen-Bregman LogDet Divergence with Application to Efficient Similarity Search for Covariance Matrices

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Sided and Symmetrized Bregman Centroids

Nielsen, F., & Nock, R. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2018176 · Cited by 164 · see citing works

The Burbea-Rao and Bhattacharyya Centroids

Nielsen, F., & Boltz, S. (2011) · IEEE Transactions on Information Theory · doi:10.1109/tit.2011.2159046 · Cited by 162 · see citing works

On a Generalization of the Jensen–Shannon Divergence and the Jensen–Shannon Centroid

Nielsen, F. (2020) · Entropy · doi:10.3390/e22020221 · Cited by 148 · see citing works

Bregman Voronoi Diagrams

Boissonnat, J., Nielsen, F., & Nock, R. (2010) · Discrete & Computational Geometry · doi:10.1007/s00454-010-9256-1 · Cited by 142 · see citing works

Information geometry of divergence functions

Амари, Ш.-И., & Cichocki, A. S. (2010) · Bulletin of the Polish Academy of Sciences Technical Sciences · doi:10.2478/v10175-010-0019-1 · Cited by 140 · see citing works

On the chi square and higher-order chi distances for approximating f-divergences

Nielsen, F., & Nock, R. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2288355 · Cited by 135 · see citing works

Statistical exponential families: A digest with flash cardsPreprint

Nielsen, F., & Garcia, V. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0911.4863 · Cited by 134 · see citing works

Geometry of minimum contrast

Eguchi, S. (1992) · Hiroshima Mathematical Journal · doi:10.32917/hmj/1206128508 · Cited by 122 · see citing works

Integration of Stochastic Models by Minimizing α-Divergence

Амари, Ш.-И. (2007) · Neural Computation · doi:10.1162/neco.2007.19.10.2780 · Cited by 117 · see citing works

The Chernoff lower bound for symmetric quantum hypothesis testingPreprint

Nussbaum, M., & Szkola, A. (2009) · Cited by 111 · see citing works

A closed-form expression for the Sharma–Mittal entropy of exponential families

Nielsen, F., & Nock, R. (2011) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8113/45/3/032003 · Cited by 93 · see citing works

An Information-Geometric Characterization of Chernoff Information

Nielsen, F. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2243726 · Cited by 84 · see citing works

Guaranteed Bounds on Information-Theoretic Measures of Univariate Mixtures Using Piecewise Log-Sum-Exp Inequalities

Nielsen, F., & Sun, K. (2016) · Entropy · doi:10.3390/e18120442 · Cited by 81 · see citing works

A family of statistical symmetric divergences based on Jensen's inequalityPreprint

Nielsen, F. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.4004 · Cited by 59 · see citing works

Quantum skew divergence

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Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems

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The Method of Maximum Entropy

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Chernoff information of exponential familiesPreprint

Nielsen, F. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1102.2684 · Cited by 53 · see citing works

Total Jensen divergences: Definition, properties and clustering

Nielsen, F., & Nock, R. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178324 · Cited by 47 · see citing works

Generalized Bhattacharyya and Chernoff upper bounds on Bayes error using quasi-arithmetic means

Nielsen, F. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.01.002 · Cited by 46 · see citing works

Closed-Form Information-Theoretic Divergences for Statistical Mixtures

Nielsen, F. (2013) · Cited by 40 · see citing works

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

Nielsen, F. (2021) · Entropy · doi:10.3390/e23040464 · Cited by 38 · see citing works

Mixture Models, Bayes Fisher Information, and Divergence Measures

Asadi, M., Ebrahimi, N. B., Kharazmi, O., & Soofi, E. S. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2018.2877608 · Cited by 36 · see citing works

Closed-Form Jensen-Renyi Divergence for Mixture of Gaussians and Applications to Group-Wise Shape Registration

Wang, F., Syeda-Mahmood, T. F., Vemuri, B. C., Beymer, D., & Rangarajan, A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04268-3_80 · Cited by 34 · see citing works

Information Geometry for Landmark Shape Analysis: Unifying Shape Representation and Deformation

Peter, A. M., & Rangarajan, A. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.69 · Cited by 34 · see citing works

ON METRIC DIVERGENCES OF PROBABILITY MEASURES

Vajda, I. (2009) · Czech digital mathematics library · Cited by 33 · see citing works

Multimodal Generative Learning Utilizing Jensen-Shannon DivergencePreprint

Sutter, T. M., Daunhawer, I., & Vogt, J. E. (2020) · arXiv (Cornell University) · doi:10.3929/ethz-b-000456059 · Cited by 29 · see citing works

On Clustering Histograms with k-Means by Using Mixed α-Divergences

Nielsen, F., Nock, R., & Амари, Ш.-И. (2014) · Entropy · doi:10.3390/e16063273 · Cited by 29 · see citing works

Metrics defined by Bregman Divergences

Chen, P., Chen, Y., & Rao, M. (2008) · Communications in Mathematical Sciences · doi:10.4310/cms.2008.v6.n4.a6 · Cited by 29 · see citing works

Quantum Skew Divergence

Audenaert, K. M. R. (2015) · Cited by 29 · see citing works

Hamilton-Jacobi approach to potential functions in information geometry

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Optimal Interval Clustering: Application to Bregman Clustering and Statistical Mixture Learning

Nielsen, F., & Nock, R. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2333001 · Cited by 27 · see citing works

Jeffreys Centroids: A Closed-Form Expression for Positive Histograms and a Guaranteed Tight Approximation for Frequency Histograms

Nielsen, F. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2260538 · Cited by 26 · see citing works

The Burbea-Rao and Bhattacharyya centroids

Nielsen, F. (2010) · SciVee · doi:10.4016/18798.01 · Cited by 26 · see citing works

Dual differential geometry associated with the Kullbaek-Leibler information on the Gaussian distributions and its 2-parameter deformations

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Bregman Clustering for Separable Instances

Ackermann, M. R., & Blömer, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-13731-0_21 · Cited by 24 · see citing works

Strong convexity of sandwiched entropies and related optimization problems

Bhatia, R., Jain, T., & Lim, Y. (2018) · Reviews in Mathematical Physics · doi:10.1142/s0129055x18500149 · Cited by 22 · see citing works

Information-theoretic matching of two point sets

Wang, Y., Woods, K., & McClain, M. (2002) · IEEE Transactions on Image Processing · doi:10.1109/tip.2002.801120 · Cited by 22 · see citing works

Generalizing Skew Jensen Divergences and Bregman Divergences With Comparative Convexity

Nielsen, F., & Nock, R. (2017) · IEEE Signal Processing Letters · doi:10.1109/lsp.2017.2712195 · Cited by 21 · see citing works

On Voronoi Diagrams on the Information-Geometric Cauchy Manifolds

Nielsen, F. (2020) · Entropy · doi:10.3390/e22070713 · Cited by 19 · see citing works

Mining Matrix Data with Bregman Matrix Divergences for Portfolio Selection

Nock, R., Magdalou, B., Briys, É., & Nielsen, F. (2012) · Springer eBooks · doi:10.1007/978-3-642-30232-9_15 · Cited by 16 · see citing works

Path Connectedness on a Space of Probability Density Functions

Eguchi, S., & Komori, O. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-25040-3_66 · Cited by 15 · see citing works

Constraining Variational Inference with Geometric Jensen-Shannon DivergencePreprint

Deasy, J., Simidjievski, N., & Lió, P. (2020) · arXiv (Cornell University) · doi:10.17863/cam.65769 · Cited by 15 · see citing works

On the Geometry of Mixtures of Prescribed Distributions

Nielsen, F., & Nock, R. (2018) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2018.8461869 · Cited by 15 · see citing works

Monte Carlo Information-Geometric Structures

Nielsen, F., & Hadjeres, G. (2018) · Signals and communication technology · doi:10.1007/978-3-030-02520-5_5 · Cited by 14 · see citing works

Reference duality and representation duality in information geometry

Zhang, J. (2015) · AIP conference proceedings · doi:10.1063/1.4905972 · Cited by 12 · see citing works

The Statistical Minkowski Distances: Closed-Form Formula for Gaussian Mixture Models

Nielsen, F. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-26980-7_37 · Cited by 12 · see citing works

Monte Carlo Information Geometry: The dually flat casePreprint

Nielsen, F., & Hadjeres, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07225 · Cited by 10 · see citing works

Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review

Nielsen, F. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39140-8_1 · Cited by 8 · see citing works

Metrics defined by Bregman divergences: Part 2

Chen, P., Chen, Y., & Rao, M. (2008) · Communications in Mathematical Sciences · doi:10.4310/cms.2008.v6.n4.a7 · Cited by 8 · see citing works

On the discontinuity of the Shannon information measures

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Information Geometry Associated with Generalized Means

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On power chi expansions of $f$-divergencesPreprint

Nielsen, F., & Hadjeres, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05818 · Cited by 3 · see citing works

RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection

Xu, C., Wang, J., Yang, W. T., Yu, H., Yu, L., & Xia, G.-S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_31 · Cited by 350 · see citing works

An Elementary Introduction to Information Geometry

Nielsen, F. (2020) · Entropy · doi:10.3390/e22101100 · Cited by 238 · see citing works

On a Generalization of the Jensen–Shannon Divergence and the Jensen–Shannon Centroid

Nielsen, F. (2020) · Entropy · doi:10.3390/e22020221 · Cited by 148 · see citing works

DP-TrajGAN: A privacy-aware trajectory generation model with differential privacy

Zhang, J., Huang, Q., Huang, Y., Ding, Q., & Tsai, P. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.12.027 · Cited by 72 · see citing works

Generalized Jensen-Shannon Divergence Loss for Learning with Noisy LabelsPreprint

Englesson, E., & Azizpour, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.04522 · Cited by 69 · see citing works

Seeded Sequential LDA: A Semi-Supervised Algorithm for Topic-Specific Analysis of Sentences

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A General Dynamic Knowledge Distillation Method for Visual Analytics

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(H)DPGMM: a hierarchy of Dirichlet process Gaussian mixture models for the inference of the black hole mass function

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Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions

Hou, Y., Volk, R., Chen, M., & Soibelman, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103554 · Cited by 48 · see citing works

Small Sample Reliability Assessment With Online Time-Series Data Based on a Worm Wasserstein Generative Adversarial Network Learning Method

Sun, B., Wu, Z., Feng, Q., Wang, Z., Ren, Y., Yang, D., & Xia, Q. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3168667 · Cited by 41 · see citing works

Attention-based clinical note summarization

Kanwal, N., & Rizzo, G. F. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507256 · Cited by 40 · see citing works

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

Nielsen, F. (2021) · Entropy · doi:10.3390/e23040464 · Cited by 38 · see citing works

Hyperspectral Anomaly Detection Based on Empirical Mode Decomposition and Local Weighted Contrast

Zhao, D., Yan, W., You, M., Zhang, J., Arun, P. V., Jiao, C., Wang, Q., & Zhou, H. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3455258 · Cited by 36 · see citing works

A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges

Megahed, M., & Mohammed, A. (2023) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1629 · Cited by 37 · see citing works

Generative Adversarial Networks

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A guide to similarity measures and their data science applications

Levy, A., Shalom, B. R., & Chalamish, M. (2025) · Journal Of Big Data · doi:10.1186/s40537-025-01227-1 · Cited by 33 · see citing works

Multimodal Generative Learning Utilizing Jensen-Shannon DivergencePreprint

Sutter, T. M., Daunhawer, I., & Vogt, J. E. (2020) · arXiv (Cornell University) · doi:10.3929/ethz-b-000456059 · Cited by 29 · see citing works

Deep representation learning for clustering longitudinal survival data from electronic health records

Qiu, J., Hu, Y., Li, L., Erzurumluoglu, A. M., Brænne, I., Whitehurst, C. E., Schmitz, J., Arora, J., Bartholdy, B., Gandhi, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-56625-z · Cited by 29 · see citing works

Simplified Fréchet Distance for Generative Adversarial Nets

Kim, C.-I., Kim, M., Jung, S., & Hwang, E. (2020) · Sensors · doi:10.3390/s20061548 · Cited by 27 · see citing works

Gamma amplitude is coupled to opposed hippocampal theta-phase states during the encoding and retrieval of episodic memories in humans

di Chanaz, L. S.-A., Pérez-Bellido, A., Wu, X., Lozano‐Soldevilla, D., Pacheco, D., Lehongre, K., Blanco, E. C., Roldán, P., Adam, C., Lambrecq, V., et al. (2023) · Current Biology · doi:10.1016/j.cub.2023.03.073 · Cited by 26 · see citing works

Ship Detection in SAR Images Based on Multilevel Superpixel Segmentation and Fuzzy Fusion

Sun, Q., Liu, M., Chen, S., Lu, F., & Xing, M. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3266373 · Cited by 25 · see citing works

SAT: A Selective Adversarial Training Approach for WiFi-Based Human Activity Recognition

Pan, Y., Zhou, Z.-P., Gong, W., & Fang, Y.-G. (2024) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2024.3420405 · Cited by 16 · see citing works

Jensen–Shannon divergence based novel loss functions for Bayesian neural networks

Thiagarajan, P., & Ghosh, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129115 · Cited by 23 · see citing works

Simulation of the complex dynamics of mean-field p -spin models using measurement-based quantum feedback control

Muñoz-Arias, M. H., Deutsch, I., Jessen, P., & Poggi, P. M. (2020) · Physical Review A · doi:10.1103/physreva.102.022610 · Cited by 24 · see citing works

Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review

Garcia, C. M., Abilio, R., Koerich, A. L., Britto, A. D. S., & Barddal, J. P. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3704922 · Cited by 24 · see citing works

Thermal information and teleportation in two-qutrit Heisenberg XX chain model

Omri, M., Abd‐Rabbou, M. Y., Khalil, E. M., & Abdel‐Khalek, S. (2022) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.01.051 · Cited by 21 · see citing works

Synthetic data generation for a longitudinal cohort study – evaluation, method extension and reproduction of published data analysis results

Langnickel, L., Schneider, J., Perrar, I., Adams, T., Moazemi, S., Praßer, F., Nöthlings, U., Fröhlich, H., & Fluck, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-62102-2 · Cited by 21 · see citing works

Effect of probiotic supplementation on the gut microbiota diversity in healthy populations: a systematic review and meta-analysis of randomised controlled trials

Éliás, A. J., Földvári-Nagy, K. C., Al-Gharati, Y. Z., Veres, D. S., Schnabel, T., Teutsch, B., Erőss, B., Hegyi, P., Lenti, K., & Földvári-Nagy, L. (2026) · BMC Medicine · doi:10.1186/s12916-025-04602-0 · Cited by 20 · see citing works

A hybrid sequential sampling strategy for sparse polynomial chaos expansion based on compressive sampling and Bayesian experimental design

Zhang, B.-Y., & Ni, Y. (2021) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2021.114130 · Cited by 19 · see citing works

Revisiting Chernoff Information with Likelihood Ratio Exponential Families

Nielsen, F. (2022) · Entropy · doi:10.3390/e24101400 · Cited by 19 · see citing works

Broken detailed balance and entropy production in directed networks

Nartallo-Kaluarachchi, R., Asllani, M., Deco, G., Kringelbach, M. L., Goriely, A., & Lambiotte, R. (2024) · Physical review. E · doi:10.1103/physreve.110.034313 · Cited by 19 · see citing works

CURATING: A multi-objective based pruning technique for CNNs

Pattanayak, S., Nag, S., & Mittal, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102031 · Cited by 19 · see citing works

Multi-Fidelity Adaptive Sampling for Surrogate-Based Optimization and Uncertainty Quantification

Garbo, A., Parekh, J., Rischmann, T., & Bekemeyer, P. (2024) · Aerospace · doi:10.3390/aerospace11060448 · Cited by 18 · see citing works

Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer

Shehzadi, T., Ifza, I., Liwicki, M., Stricker, D., & Afzal, M. Z. (2026) · Sensors · doi:10.3390/s26010310 · Cited by 18 · see citing works

Dynamic fine‐tuning layer selection using Kullback – Leibler divergence

Wanjiku, R. N., Nderu, L., & Kimwele, M. W. (2022) · Engineering Reports · doi:10.1002/eng2.12595 · Cited by 18 · see citing works

A Simple Approximation Method for the Fisher–Rao Distance between Multivariate Normal Distributions

Nielsen, F. (2023) · Entropy · doi:10.3390/e25040654 · Cited by 17 · see citing works

On the Computation of the Gaussian Rate–Distortion–Perception Function

Serra, G., Stavrou, P. A., & Kountouris, M. (2024) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2024.3381230 · Cited by 17 · see citing works

Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

Shafqat, W., & Byun, Y.-C. (2021) · Applied Sciences · doi:10.3390/app11041366 · Cited by 16 · see citing works

Divergence metrics for determining optimal training sample size in digital soil mapping

Saurette, D. D., Heck, R. J., Gillespie, A., van den Berg, A. A., & Biswas, A. (2023) · Geoderma · doi:10.1016/j.geoderma.2023.116553 · Cited by 15 · see citing works

Information Theoretic Causal Effect Quantification

Wieczorek, A., & Röth, V. (2019) · Entropy · doi:10.3390/e21100975 · Cited by 15 · see citing works

Enhancement of risk informed validation framework for external hazard scenario

Bodda, S. S., Gupta, A., & Dinh, N. T. (2020) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2020.107140 · Cited by 15 · see citing works

Probiotics mediated gut microbiota diversity shifts are associated with reduction in histopathology and shedding of Lawsonia intracellularis

Muwonge, A., Karuppannan, A. K., & Opriessnig, T. (2021) · Animal Microbiome · doi:10.1186/s42523-021-00084-6 · Cited by 15 · see citing works

Constraining Variational Inference with Geometric Jensen-Shannon DivergencePreprint

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A Complete Review on the Application of Statistical Methods for Evaluating Internet Traffic Usage

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Uncovering dark matter density profiles in dwarf galaxies with graph neural networks

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DenVar: density-based variation analysis of multiplex imaging data

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RSSI Probability Density Functions Comparison Using Jensen-Shannon Divergence and Pearson Distribution

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Robust Active Contour Model Using Patch-Based Signed Pressure Force and Optimized Fractional-Order Edge

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Detection and clustering of urban form types with machine learning: insights into Thessaloniki's urban planning and evolution

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A thorough assessment of the non-IID data impact in federated learning

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Assessing groundwater behavior and future trends in the Ardabil Aquifer: A comparative study of groundwater modeling system and categorical gradient boosting hybrid model

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Dataset of Program Source Codes Solving Unique Programming Exercises Generated by Digital Teaching Assistant

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Fast Approximations of the Jeffreys Divergence between Univariate Gaussian Mixtures via Mixture Conversions to Exponential-Polynomial Distributions

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Automated navigation of condensate phase behavior with active machine learning

Leurs, Y. H. A., van den Hout, W., Gardin, A., van Dongen, J. L. J., Rodriguez‐Abetxuko, A., Erkamp, N. A., van Hest, J. C. M., Grisoni, F., & Brunsveld, L. (2025) · Nature Communications · doi:10.1038/s41467-025-64617-2 · Cited by 13 · see citing works

DSLSM: Dual-kernel-induced statistic level set model for image segmentation

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Multilevel irreversibility reveals higher-order organization of nonequilibrium interactions in human brain dynamics

Nartallo-Kaluarachchi, R., Bonetti, L., Fernández‐Rubio, G., Vuust, P., Deco, G., Kringelbach, M. L., Lambiotte, R., & Goriely, A. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2408791122 · Cited by 11 · see citing works

A taxonomy for similarity metrics between Markov decision processes

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A Similarity Matrix for Preserving Haplotype Diversity Amongst Parents in Genomic Selection

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On f-Divergences Between Cauchy Distributions

Nielsen, F., & Okamura, K. (2022) · IEEE Transactions on Information Theory · doi:10.1109/tit.2022.3231645 · Cited by 11 · see citing works

A closed-form formula for the Kullback-Leibler divergence between Cauchy distributionsPreprint

Chyzak, F., & Nielsen, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.10965 · Cited by 16 · see citing works

Quantum-Cognitive Neural Networks: Assessing Confidence and Uncertainty with Human Decision-Making Simulations

Maksimovic, M., & Maksymov, I. S. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9010012 · Cited by 13 · see citing works

Seeing the Unseen in Micro-Video Popularity Prediction: Self-Correlation Retrieval for Missing Modality Generation

Cheng, Z., Lang, J., Zhong, T., & Zhou, F. (2025) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3690624.3709308 · Cited by 10 · see citing works

An Information-Theoretic Measure for Balance Assessment in Comparative Clinical Studies

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Transport information Bregman divergences

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Go Beyond Black-box Policies: Rethinking the Design of Learning Agent for Interpretable and Verifiable HVAC Control

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Spiking Patterns in the Globus Pallidus Highlight Convergent Neural Dynamics across Diverse Genetic Dystonia Syndromes

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Adaptive kernel density estimation proposal in gravitational wave data analysis

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Visual Prediction of Tropical Cyclones with Deep Convolutional Generative Adversarial Networks

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Compositional Safe Approximation of Response Time Probability Density Function of Complex Workflows

Carnevali, L., Paolieri, M., Reali, R., & Vicario, E. (2023) · ACM Transactions on Modeling and Computer Simulation · doi:10.1145/3591205 · Cited by 9 · see citing works

Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationPreprint

Han, S., & Sung, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.01419 · Cited by 9 · see citing works

When Invariant Representation Learning Meets Label Shift: Insufficiency and Theoretical Insights

Luo, Y.-W., & Ren, C.-X. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3417214 · Cited by 8 · see citing works

Improving automatic defect recognition on GDXRay castings dataset by introducing GenAI synthetic training data

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Crossover point analysis with Jensen-Shannon divergence lower bound for bi-objective reliability optimization of k-out-of-n system

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Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection

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Quantitative Assessment of Soldering-Induced PM2.5 Exposure Using a Distributed Sensor Network in Instructional Laboratory Settings

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Inferring Cosmological Parameters with Evidential Physics-Informed Neural Networks

Tan, H. S. (2025) · Universe · doi:10.3390/universe11120403 · Cited by 7 · see citing works

Correlation Structure and Co-Movement of Hunan Province’s Air Pollution: Evidence from the Multiscale Temporal Networks

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Two-step AI-aided Bayesian source identification of urban-scale pollution

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Forecasting dynamics by an incomplete equation of motion and an auto-encoder Koopman operator

Chen, Z., Sun, H., & Xiong, W. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111599 · Cited by 7 · see citing works

Tale of Two Polymorphs: Investigating the Structural Differences and Dynamic Relationship between Nirmatrelvir Solid Forms (Paxlovid)

Sadeghi, M. S., Guo, R., Bellucci, M. A., Quino, J., Buckle, E. L., Nisbet, M. L., Yang, Z., Greenwell, C., Gorka, D. E., Pickard, F. C., et al. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01074 · Cited by 7 · see citing works

Automated live-cell single-molecule tracking in enteroid monolayers reveals transcription factor dynamics probing lineage-determining function

Walther, N., Anantakrishnan, S., Graham, T. G. W., Dailey, G. M., Tjian, R., & Darzacq, X. (2024) · Cell Reports · doi:10.1016/j.celrep.2024.114914 · Cited by 7 · see citing works

Reconstructing parametric gravitational-wave population fits from nonparametric results without refitting the data

Fabbri, C. M., Gerosa, D., Santini, A., Mould, M., Toubiana, A., & Gair, J. R. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.104053 · Cited by 7 · see citing works

Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

Yun, J., Basak, M., & Han, M.-M. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.017266 · Cited by 7 · see citing works

Agent-oriented data clustering for medical monitoring

Strilets, V., Donets, V., Ugryumov, M., Artiuch, S., Zelenskyi, R., & Goncharova, T. (2022) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2022.1.08 · Cited by 7 · see citing works

Improving semantic segmentation accuracy in thin cloud interference scenarios by mixing simulated cloud-covered samples

Wang, H., Li, J., Shen, Z., Zhang, Z., Bai, L., Li, R., Zhou, C., Maeyer, P. D., & de Voorde, T. V. (2024) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2024.104087 · Cited by 6 · see citing works

Learning geometric Jensen-Shannon divergence for tiny object detection in remote sensing images

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Can Sequential Images from the Same Object Be Used for Training Machine Learning Models? A Case Study for Detecting Liver Disease by Ultrasound Radiomics

Sultan, L. R., Cary, T. W., Al‐Hasani, M., Karmacharya, M. B., Venkatesh, S. S., Assenmacher, C., Radaelli, E., & Sehgal, C. M. (2022) · AI · doi:10.3390/ai3030043 · Cited by 6 · see citing works

Using a topic model to map and analyze a large curriculum

Takizawa, P. A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0284513 · Cited by 6 · see citing works

Anomaly Detection in Student Activity in Solving Unique Programming Exercises: Motivated Students against Suspicious Ones

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Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation

Balasubramanian, V., Kobyzev, I., Bahuleyan, H., Shapiro, I., & Olga Vechtomova, A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.32 · Cited by 6 · see citing works

Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity

Bhandari, S., Lencastre, P., Mathema, R., Szorkovszky, A., Yazidi, A., & Lind, P. G. (2025) · Scientific Reports · doi:10.1038/s41598-025-05286-5 · Cited by 5 · see citing works

FedHSQA: Robust Aggregation in Hierarchical Federated Learning via Anomaly Scoring-Based Adaptive Quantization for IoV

Xing, L., Luo, Z., Deng, K., Wu, H., Ma, H., & Lu, X. (2025) · Electronics · doi:10.3390/electronics14081661 · Cited by 5 · see citing works

Complex Belief Divergence Measures for Multisource Information Fusion

Huang, J., & Xiao, F. (2024) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2024.3490183 · Cited by 5 · see citing works

Automatic classification of HEp-2 specimens by explainable deep learning and Jensen-Shannon reliability index

Mencattini, A., Tocci, T., Nuccetelli, M., Pieri, M., Bernardini, S., & Martinelli, E. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.103030 · Cited by 5 · see citing works

Of Traits and Trees: Probabilistic Distances under Continuous Trait Models for Dissecting the Interplay among Phylogeny, Model, and Data

Adams, R. H. J., Blackmon, H., & DeGiorgio, M. (2021) · Systematic Biology · doi:10.1093/sysbio/syab009 · Cited by 5 · see citing works

Statistical levelling of multi-element geochemical data

Williams, P. M. (2021) · Applied Computing and Geosciences · doi:10.1016/j.acags.2021.100060 · Cited by 5 · see citing works

Transmission distance in the space of quantum channels

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Solar Wind Structures from the Gaussianity of Magnetic Magnitude

Huang, Z., Shi, C., Velli, M., Sioulas, N., Panasenco, O., Bowen, T. A., Matteini, L., 夏, M. 铭. X., Shi, X., Huang, S., et al. (2024) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ad72f1 · Cited by 5 · see citing works

Impact of the observation frequency coverage on the significance of a gravitational wave background detection in pulsar timing array data

Ferranti, I., Falxa, M., Sesana, A., Chalumeau, A., Porayko, N. K., Shaifullah, G. M., Cognard, I., Guillemot, L., Kramer, M., Liu, K., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202452805 · Cited by 5 · see citing works

Agile Climate-Sensor Design and Calibration Algorithms Using Machine Learning: Experiments From Cape Point

Barrett, T., & Mishra, A. K. (2023) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc53148.2023.10176000 · Cited by 5 · see citing works

A family of statistical symmetric divergences based on Jensen's inequalityPreprint

Nielsen, F. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.4004 · Cited by 59 · see citing works

On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius

Nielsen, F. (2021) · Entropy · doi:10.3390/e23040464 · Cited by 38 · see citing works

On Unified Generalizations of Relative Jensen-Shannon and Arithmetic-Geometric Divergence Measures and TheirPreprint

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A Short Note on the Jensen-Shannon Divergence between Simple Mixture DistributionsPreprint

Geiger, B. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.02059

Two tales for a geometric Jensen--Shannon divergencePreprint

Nielsen, F. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2508.05066