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

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

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α-Geodesical Skew Divergence

Kimura, M., & Hino, H. (2021) · Entropy · doi:10.3390/e23050528 · Cited by 5

Two Types of Geometric Jensen–Shannon Divergences

Nielsen, F. (2025) · Entropy · doi:10.3390/e27090947 · Cited by 4

Through-Wall Imaging Based On WiFi Channel State Information

Strohmayer, J., Sterzinger, R., Stippel, C., & Kampel, M. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647775 · Cited by 4

Novel symmetric divergence based importance measures for engineering simulation models under uncertainty

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Comparison of structural connectomes for modeling deep brain stimulation pathway activation

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Advancements and challenges in the development of generative adversarial network (GANs) for deep learning

Iqbal, K., Rafique, A., Qaisar, S., & Tabassum, M. (2025) · Discover Networks · doi:10.1007/s44354-025-00007-w · Cited by 4

Solving Decision-Making Problems Using a Measure for Information Values Connected to the Equilibrium Points (IVEP) MCDM Method and Zakeri–Konstantas Performance Correlation Coefficient

Zakeri, S., & Konstantas, D. (2022) · Information · doi:10.3390/info13110512 · Cited by 4

When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting

Kamarthi, H., Kong, L., Rodríguez, A., Zhang, C., & Prakash, B. A. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599547 · Cited by 4

Berezinskii-Kosterlitz-Thouless transition from neural network flows

Ng, K.-K., Huang, C.-Y., & Lin, F.-L. (2023) · Physical review. E · doi:10.1103/physreve.108.034104 · Cited by 4

In-Training Explainability Frameworks: A Method to Make Black-Box Machine Learning Models More Explainable

Acun, C., & Nasraoui, O. (2023) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat59888.2023.00036 · Cited by 4

A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning

Zhang, X., Kang, Y., Fan, L., Chen, K., & Yang, Q. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3652612 · Cited by 8

False Data Detection in Power System Under State Variables' Cyber Attacks Using Information Theory

Parizad, A., & Hatziadoniu, C. J. (2021) · IEEE Power and Energy Conference at Illinois (PECI) · doi:10.1109/peci51586.2021.9435204 · Cited by 4

A non-parametric evaluation method in staircase test for improving fatigue limit assessment

Shi, L., Khalij, L., Bai, H., & Gautrelet, C. (2022) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2022.103373 · Cited by 4

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Carnevali, L., Paolieri, M., Reali, R., & Vicario, E. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85172-9_5 · Cited by 4

q-Paths: Generalizing the Geometric Annealing Path using Power MeansPreprint

Masrani, V., Brekelmans, R., Bui, T. D., Nielsen, F., Galstyan, A., Steeg, G. V., & Wood, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.00745 · Cited by 4

CIM-PPO:Proximal Policy Optimization with Liu-Correntropy Induced MetricPreprint

Guo, Y., Long, H., Duan, X., Feng, K., Li, M., & Ma, X. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10522 · Cited by 6

Multilevel irreversibility reveals higher-order organisation of non-equilibrium interactions in human brain dynamicsPreprint

Nartallo-Kaluarachchi, R., Bonetti, L., Fernández‐Rubio, G., Vuust, P., Deco, G., Kringelbach, M. L., Lambiotte, R., & Goriely, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.05.02.592195 · Cited by 4

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Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space

Vazquez-Salazar, L. I., & Meuwly, M. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00752 · Cited by 3

Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network

Sipps, J., & Magruder, L. A. (2023) · Remote Sensing · doi:10.3390/rs15235594 · Cited by 3

Community knowledge graph abstraction for enhanced link prediction: A study on PubMed knowledge graph

Zhao, Y., Bollegala, D., Hirose, S., Jin, Y., & Kozu, T. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104725 · Cited by 3

Explainable representative-days clustering on low-voltage grid meters and feeders, with noise-aware multi-objective Bayesian optimization, applied to grid-congestion events

Theodorakos, K., Agudelo, O. M., Becker, T., Vanthournout, K., D’hulst, R., & Moor, B. D. (2025) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2025.101622 · Cited by 3

Pollen image manipulation and projection using latent space

Mills, B., Zervas, M. N., & Grant‐Jacob, J. A. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1539128 · Cited by 3

Endpoint carbon content and temperature prediction model in BOF steel-making based on dynamic feature partitioning − weighted ensemble learning

Wang, H., Liu, H., Chen, F., Li, H., & Xue, X. (2025) · Metallurgical Research & Technology · doi:10.1051/metal/2025026 · Cited by 3

Posterior Averaging Information Criterion

Zhou, S. (2023) · Entropy · doi:10.3390/e25030468 · Cited by 3

Statistical complexity and the road to equilibrium in many-body chaotic quantum systems

Muñoz-Arias, M. H. (2022) · Physical review. E · doi:10.1103/physreve.106.044103 · Cited by 4

Aggregation of Leslie matrix models with application to ten diverse animal species

Hinrichsen, R. A. (2023) · Population Ecology · doi:10.1002/1438-390x.12149 · Cited by 3

Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion

Yadav, V., Ghosh, S., & Miller, C. E. (2023) · Geoscientific model development · doi:10.5194/gmd-16-5219-2023 · Cited by 3

Synthetic Time Series Generation for Decision Intelligence Using Large Language Models

Grigoraș, A., & Leon, F. (2024) · Mathematics · doi:10.3390/math12162494 · Cited by 3

Communication-Efficient Soft Actor–Critic Policy Collaboration via Regulated Segment Mixture

Yu, X., Li, R., Liang, C., & Zhao, Z. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3481257 · Cited by 3

Temporal Comparisons Involving Paleoclimate Data Assimilation: Challenges and Remedies

Emile‐Geay, J., Hakim, G. J., Viens, F. G., Zhu, F., & Amrhein, D. E. (2024) · Journal of Climate · doi:10.1175/jcli-d-24-0101.1 · Cited by 6

Accurate Transcription Factor Activity Inference to Decipher Cell Identity from Single‐Cell Transcriptomic Data with MetaTF

Hu, Y., Zhu, Y., Tang, G., Shan, M., Tan, P., Yi, Y., Zhang, X., Liu, M., Li, X., Wu, L., et al. (2025) · Advanced Science · doi:10.1002/advs.202410745 · Cited by 3

An analytical proof on suitability of Cauchy-Schwarz Divergence as the aggregation criterion in Region Growing Algorithm

Baleghi, Y., & Rousseau, D. (2021) · Image and Vision Computing · doi:10.1016/j.imavis.2021.104312 · Cited by 3

ProcessM: Intelligent Process Mining software

Pawlak, T. P., & Potoniec, J. (2025) · SoftwareX · doi:10.1016/j.softx.2025.102079 · Cited by 3

Importance-Guided Interpretability and Pruning for Video Transformers in Driver Action Recognition

Panadero, R., Schörkhuber, D., & Gelautz, M. (2025) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv61041.2025.00517 · Cited by 2

From Linear Geometry to Nonlinear and Information-Geometric Settings in Test Theory: Bregman Projections as a Unifying Framework

Zumbo, B. D. (2025) · Educational and Psychological Measurement · doi:10.1177/00131644251393483 · Cited by 2

Identification of significant SNPs and the quantification of correlation using Genomic Informational Field Theory (GIFT).

Gadsby, S., Rauch, C., & Wattis, J. (2026) · Mathematical Biosciences · doi:10.1016/j.mbs.2025.109606 · Cited by 2

On Representations of Divergence Measures and Related Quantities in Exponential Families

Bedbur, S., & Kamps, U. (2021) · Entropy · doi:10.3390/e23060726 · Cited by 2

Robustness of Rock Damage Regions Induced by Crack Nucleation

Liu, Y., Wang, C., Li, C., Bai, Z., Huang, L., Peng, K., Xue, X., & Cao, P. (2023) · International Journal of Geomechanics · doi:10.1061/ijgnai.gmeng-8331 · Cited by 2

Variational Counterfactual Prediction Under Runtime Domain Corruption

Wen, H., Chen, T., Chai, L. K., Sadiq, S. W., Gao, J., & Yin, H. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3321893 · Cited by 7

Non‐turbulent motion identified from properties of transport and its influence on the calculation of turbulent flux

Liu, Z., Zhang, H., Wei, W., Cai, X., Song, Y., & Zhang, X. (2024) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.4705 · Cited by 2

Optimization Problem for Probabilistic Time Intervals of Quasi-Deterministic Output and Self-Similar Input Data Packet Flow in Telecommunication Networks

Linets, Y. P., Voronkin, R. A., Васильевич, С. Г., & Govorova, S. V. (2024) · Advanced Engineering Research · doi:10.23947/2687-1653-2024-24-4-424-432 · Cited by 2

The value of information in multi-scale feedback systems

Felice, L. J. D., Diaconescu, A., Zahadat, P., & Mellodge, P. (2025) · Frontiers in Complex Systems · doi:10.3389/fcpxs.2025.1612142 · Cited by 2

Distribution Analysis for Diagnostics and Therapeutics of Motor Actions

Srivatsa, A. V., Aghamohammadi, N. R., Ryali, P., Scarpellini, A., Kim, J. J. H., Aggarwal, A., Wright, Z. A., Huang, F. C., Reinkensmeyer, D. J., Esmailbeigi, H., et al. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3636009 · Cited by 2

Symmetrization in the Calculation Pipeline of Gauss Function-Based Modeling of Hydrophobicity in Protein Structures

Banach, M. (2022) · Symmetry · doi:10.3390/sym14091876 · Cited by 2

On the Symmetry Importance in a Relative Entropy Analysis for Some Engineering Problems

Kamiński, M. (2022) · Symmetry · doi:10.3390/sym14091945 · Cited by 2

Ollivier-Ricci Curvature for Head Pose Estimation from a Single Image

Abate, A. F., Cascone, L., Distasi, R., & Nappi, M. (2022) · IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/ijcb54206.2022.10007989 · Cited by 2

Jeffreys Divergence and Generalized Fisher Information Measures on Fokker–Planck Space–Time Random Field

Zhang, J. (2023) · Entropy · doi:10.3390/e25101445 · Cited by 2

Preference-conditioned Pixel-based AI Agent For Game Testing

Abdelfattah, S. M., Brown, A., & Zhang, P. (2023) · IEEE Conference on Games (CoG) · doi:10.1109/cog57401.2023.10333200 · Cited by 2

Multi-Regional Pelvic Floor Muscle Function Diagnosis System Based on Inflatable Stretchable Electrode Array

Chen, H., Wu, S., Wang, Y., Chang, Y., Li, M., Xie, Z., & Wang, S. (2024) · Healthcare · doi:10.3390/healthcare12191910 · Cited by 2

Information-Geometric Set Embeddings (IGSE): From Sets to Probability DistributionsPreprint

Sun, K., & Nielsen, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.12463 · Cited by 2

Likelihood Ratio Exponential FamiliesPreprint

Brekelmans, R., Nielsen, F., Makhzani, A., Galstyan, A., & Steeg, G. V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.15480 · Cited by 2

New bounds for $k$-means and information $k$-meansPreprint

Appert, G., & Catoni, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.05728 · Cited by 2

Transport information Bregman divergencesPreprint

Li, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.01162 · Cited by 2

Fast Proxy Centers for the Jeffreys Centroid: The Jeffreys–Fisher–Rao Center and the Gauss–Bregman Inductive Center

Nielsen, F. (2024) · Entropy · doi:10.3390/e26121008 · Cited by 1

Anticipating customer substitution: a data-driven, distance-based approach for out-of-stock product configurations

Fabian, M., Fischer, K., & Rüpke, J. M. (2025) · Journal of Modelling in Management · doi:10.1108/jm2-03-2024-0073 · Cited by 1

Quantitative evaluation of turbulence reconstruction algorithms for flux estimation based on the characteristics of atmospheric turbulence

Liu, Z., Zhang, H., Cai, X., & Song, Y. (2025) · Physics of Fluids · doi:10.1063/5.0279166 · Cited by 1

A Complexity-Based Approach to Quantum Observable Equilibration

Alpino, M. G., Debarba, T., Vianna, R. O., & Cesário, A. T. (2025) · Entropy · doi:10.3390/e27080824 · Cited by 1

Entropy and mechanistic concepts after intraovarian platelet-rich plasma: Experimental considerations for local tissue responses mediated by NF-κB and TNF-α

Sills, E. S., Harrity, C., Chu, H. I., Wang, J.-W., Yang, F., Tan, S. L., & Wood, S. H. (2025) · BioMolecular Concepts · doi:10.1515/bmc-2025-0054 · Cited by 1

ChatGPT as a Subject Matter Expert in the Parameterization of Bayesian Network Classifiers

Krüger, M. J. (2025) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion65864.2025.11124000 · Cited by 1

Advanced machine learning approaches for predicting energy and fossil fuel consumption for green growth

Song, L., Luo, L., & Li, J. (2025) · Unconventional Resources · doi:10.1016/j.uncres.2025.100262 · Cited by 1

EGCN: Entropy-based graph convolutional network for anomalous pattern detection and forecasting in real estate markets

Le, D. A., Rajasegarar, S., Luo, W., Nguyen, T. T., Vo, N., Nguyen, Q., & Angelova, M. N. (2025) · PLoS ONE · doi:10.1371/journal.pone.0334141 · Cited by 1

A Tool for Synthesizing and Implementing Medium Voltage Load Profiles

Plompen, H., Moura, R. A. A., Mehmood, K. K., van der Molen, A., van der Wielen, P. C. J. M., & Nguyen, P. H. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-03101-3_3 · Cited by 1

Intraday order transitions across market capitalizations through Markov analysis

Luwang, S. R., Rai, A., Nurujjaman, M., & Petroni, F. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-025-02060-6 · Cited by 1

Enhancing real estate prediction with entropy-based pattern analysis and economic sentiment integration

Le, D., Rajasegarar, S., Luo, W., Nguyen, T. T., & Angelova, M. N. (2025) · Engineering Computations · doi:10.1108/ec-09-2024-0895 · Cited by 3

A distribution-free stochastic physics-guided reliability analysis under polymorphic uncertainty

Lye, A., Ferson, S. A., & Xiao, S. (2026) · Annals of Nuclear Energy · doi:10.1016/j.anucene.2026.112191 · Cited by 1

Super-resolution of medical images with GANs: A survey

Goven, A., & Toptaş, B. (2026) · Advances in Complex Systems · doi:10.1142/s1793962326500182 · Cited by 1

AutomataGPT: Transformer‐Based Forecasting and Ruleset Inference for Two‐Dimensional Cellular Automata

Berkovich, J., David, N. S., & Buehler, M. J. (2026) · Advanced Science · doi:10.1002/advs.202511352 · Cited by 6

From Vector Space to Symbolic Space: Informational and Semantic Analysis of Benign and DDoS IoT Traffic Using LLMs

Pîrnău, M., Priescu, I., Botezatu, M.-A., Priescu, C. M., & Joița, D. (2026) · Electronics · doi:10.3390/electronics15081724 · Cited by 1

Text-to-3D Generation Using Jensen-Shannon Score Distillation

Do, K., & Hua, B. (2026) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv69130.2026.00132 · Cited by 1

Comparison of asymmetric and symmetric decision-making bases in generalised grey target decision method for mixed attributes

Ma, J. (2026) · Engineering Computations · doi:10.1108/ec-07-2025-0791 · Cited by 1

Deep Clustering using Dirichlet Process Gaussian Mixture

Lim, K. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533366 · Cited by 1

DrugGen: a database of de novo-generated molecular binders for specified target proteins

Qian, H., Zhou, J., Tu, S., & Xu, L. (2023) · Database · doi:10.1093/database/baad090 · Cited by 1

Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models

Bhandari, S., Lencastre, P., & Lind, P. G. (2024) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3638530.3664134 · Cited by 1

Evaluating the Uncertainty and Predictive Performance of Probabilistic Models Devised for Grade Estimation in a Porphyry Copper Deposit

Leung, R., Lowe, A. M., & Melkumyan, A. (2025) · Modelling—International Open Access Journal of Modelling in Engineering Science · doi:10.3390/modelling6020050 · Cited by 1

Australian Real Estate Market Price Prediction with Entropy Clustering and Sentiment-Based Deep Neural Framework

Le, D. A., Rajasegarar, S., Luo, W., Nguyen, T. T., & Angelova, M. N. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6966-0_10 · Cited by 1

Gaussian Process Model Predictive Control Based Cooperative Motion Planning Structure for UAGVS

Sun, P., Que, H., & Zhou, Y. (2020) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv50220.2020.9305472 · Cited by 1

Quasiconvex Jensen Divergences and Quasiconvex Bregman Divergences

Nielsen, F., & Hadjeres, G. (2021) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-030-77957-3_11 · Cited by 1

Learning Adversarially Robust Object Detector with Consistency Regularization in Remote Sensing Images

Li, Y., Fang, Y., Li, W., Jiang, B., Wang, S., & Li, Z. (2023) · Remote Sensing · doi:10.3390/rs15163997 · Cited by 1

CpelTdm.jl: a Julia package for targeted differential DNA methylation analysisPreprint

Abante, J., & Goutsias, J. I. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.17.343020 · Cited by 1

Ubiquitous Dynamical Time Asymmetry in Measurements on Materials and Biological SystemsPreprint

Lapolla, A., Smith, J. C., & Godec, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.01666 · Cited by 1

On the Kullback-Leibler divergence between discrete normal distributionsPreprint

Nielsen, F. (2021) · arXiv (Cornell University) · doi:10.13140/rg.2.2.27738.06085 · Cited by 1

Quantifying Tissue Response after Intraovarian Platelet-Rich Plasma: Experimental Approach to Assess NF-κB and TNF-α as Mediators to Reduce Local EntropyPreprint

Sills, E. S. (2023) · Preprints.org · doi:10.20944/preprints202308.1004.v1 · Cited by 1

Enhancing ECG Signal Data through Denoising Features with Transformer Generative Adversarial Networks for Model Classification 1D-CNNPreprint

Atmadja, H. Y. N., Bustamam, A., & Hermawan, H. (2023) · Research Square · doi:10.21203/rs.3.rs-3432401/v1 · Cited by 1

Information Theory and Gray Level Transformation Techniques in Detecting False Data Injection Attacks on Power System State Estimation

Parizad, A., & Hatziadoniu, C. J. (2025) · doi:10.1002/9781394334599.ch1 · Cited by 1

A note on the quasiconvex Jensen divergences and the quasiconvex Bregman divergences derived thereofPreprint

Nielsen, F., & Hadjeres, G. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.08857

Comment on gmd-2022-89

(2022) · Geoscientific model development · doi:10.5194/gmd-2022-89-rc1

Comment on gmd-2022-89

(2022) · Geoscientific model development · doi:10.5194/gmd-2022-89-rc2

Approximate Bayesian Inference for Educational Attainment Models

Alghamdi, S. A., Dean, N., & Ludger Evers, A. (2022) · Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...) · doi:10.11159/icsta22.140

Reply on RC2

Yadav, V. (2022) · Geoscientific model development · doi:10.5194/gmd-2022-89-ac2

Reply on RC1

Yadav, V. (2022) · Geoscientific model development · doi:10.5194/gmd-2022-89-ac1

Bespoke Mitigation Framework for False Data Injection Attack-Induced Contingency Events

Chan, S., & Nopphawan, P. (2023) · International Conference on Cyber Management and Engineering (CyMaEn) · doi:10.1109/cymaen57228.2023.10050946

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials

Vettoretti, M., Drecogna, M., Favero, S. D., Facchinetti, A., & Sparacino, G. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107700 · Cited by 2

Quasi-arithmetic Centers, Quasi-arithmetic Mixtures, and the Jensen-Shannon $$\nabla $$-Divergences

Nielsen, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-38271-0_15

Measuring phase-amplitude coupling opposition in neurophysiological signals with the Mean Opposition Vector Index (MOVI)Preprint

di Chanaz, L. S.-A., Pérez-Bellido, A., & Fuentemilla, L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.04.551929

Crystal structure search with principal invariants

Lee, I., & Shin, S. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2023.108889

Quantitative definition of chemical synthetic pathway complexity of organic compounds

Baranwal, T., Huang, H., Avadhani, U., Goyal, A., Samavedam, A., Hu, T., Kale, M., Nepani, T., Srikanth, V., Downing, R. G., et al. (2023) · Journal of Emerging Investigators · doi:10.59720/22-009

PRIME: Posterior Reconstruction of the Input for Model Explanations

Yang, Y., Zhao, Y., & Cheng, Y. (2023) · Pattern Recognition Letters · doi:10.1016/j.patrec.2023.11.009 · Cited by 1

In situ Remnants of Solar Surface Structures from Jensen-Shannon ScalogramPreprint

Huang, Z., Shi, C., Velli, M., Sioulas, N., Panasenco, O., Bowen, T. A., Matteini, L., Xia, M., Shi, X., Huang, S., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3597425/v1

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

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

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

Kumar, P., Inder, & Taneja, J. (2012)

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

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