The DOI matches its Crossref record.
On Information and Sufficiency
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“IPF minimizes the strictly convex Kullback-Leibler divergence [15, 16] (see Sect. 5.2.2) over the set of matrices with the prescribed margins.”
From Classical Paradigm of Transportation Modeling (2026) · cites this paper as [16]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“we introduce the Kullback-Leibler (KL) divergence loss [28] between the predictions of the quantized network and the full-precision network as soft labels to align their outputs, thereby recovering the performance of the quantized network”
From Task-Specific Zero-Shot Quantization-Aware Training for Object Detection (2025) · cites this paper as [28]
In the citing paper
“Rwalk improves the regularization methods of EWC and SI, and further improves the performance based on them. However, for performance reasons, some old data are still retained. This shows the effectiveness of rehearsal strategies for mitigating catastrophic forgetting.”
From An Appraisal of Incremental Learning Methods (2020) · cites this paper as [48]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Still being checked.
Research works printed but not linked: 1
- [15]15. Journal, Vol. 27 (1948), pp. 379–423; pp. 623–656. - [17] B. L. WELCH, 'Note on discriminant functions," Biometrika, Vol.31 (1939), pp. 218–219. - [16] C. E. SHANNoN AND W. WEAvER, The Mathematical Theory of Communication, University of Illinois Press, Urbana, 1949. - [18] N. WiENER, Cybernetics, John Wiley and Sons, 1948. - [19] J. L. DooB, "Statistical estimation," Trans. Am. Math. Soc., Vol. 39 (1936), pp. 410–421. - [20] E. L. LEHmANN, "Some principles of the theory of testing hypotheses," Annals of Math. Stat., Vol. 21 (1950), pp. 1–26.
Cited by
Sorted by citations
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Bayesian Measures of Model Complexity and Fit
Spiegelhalter, D. J., Best, N. G., Carlin, B. P., & Van Der Linde, A. (2002) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00353 · Cited by 151
Multimodel Inference: Understanding AIC and BIC in Model Selection
Burnham, K. P., & Anderson, D. R. (2004) · Sociological Methods & Research · doi:10.1177/0049124104268644 · Cited by 11,648
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847
A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678
Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
Vuong, Q. H. (1989) · Econometrica · doi:10.2307/1912557 · Cited by 5,929
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Testing the number of components in a normal mixture
Lo, Y. (2001) · Biometrika · doi:10.1093/biomet/88.3.767 · Cited by 4,640
Bozdogan, H. (1987) · Psychometrika · doi:10.1007/bf02294361 · Cited by 4,468
Burnham, K. P., Anderson, D. R., & Huyvaert, K. P. (2011) · Behavioral Ecology and Sociobiology · doi:10.1007/s00265-010-1029-6 · Cited by 4,104
Posada, D., & Buckley, T. R. (2004) · Systematic Biology · doi:10.1080/10635150490522304 · Cited by 3,927
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
Understanding normal and impaired word reading: Computational principles in quasi-regular domains.
Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996) · Psychological Review · doi:10.1037/0033-295x.103.1.56 · Cited by 2,698
A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., et al. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.05.008 · Cited by 3,111
AIC model selection using Akaike weights
Wagenmakers, E.-J., & Farrell, S. (2004) · Psychonomic Bulletin & Review · doi:10.3758/bf03206482 · Cited by 2,684
Assessing the accuracy of prediction algorithms for classification: an overview
Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A. F., & Nielsen, H. (2000) · Bioinformatics · doi:10.1093/bioinformatics/16.5.412 · Cited by 2,111
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
Landrieu, L., & Simonovsky, M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00479 · Cited by 1,543
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
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
Wang, X., Han, Y., Leung, V. C. M., Niyato, D., Yan, X., & Chen, X. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2970550 · Cited by 1,498
The Theory of Quantum Information
Watrous, J. (2018) · doi:10.1017/9781316848142 · Cited by 1,325
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Chaudhry, A., Dokania, P. K., Ajanthan, T., & Torr, P. H. S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01252-6_33 · Cited by 1,091
Predicting clicks: estimating the click-through rate for new ads
Richardson, M., Dominowska, E., & Ragno, R. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242643 · Cited by 1,049
The Variational Quantum Eigensolver: A review of methods and best practices
Tilly, J., Chen, H., Cao, S., Picozzi, D., Setia, K., Li, Y., Grant, E., Wossnig, L., Rungger, I., Booth, G. H., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.08.003 · Cited by 1,031
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895
Sim, S., Johnson, P. D., & Aspuru‐Guzik, A. (2019) · Advanced Quantum Technologies · doi:10.1002/qute.201900070 · Cited by 972
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., & Bennamoun, M. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2022.3155327 · Cited by 879
Analyzing ordinal data with metric models: What could possibly go wrong?
Liddell, T. M., & Kruschke, J. K. (2018) · Journal of Experimental Social Psychology · doi:10.1016/j.jesp.2018.08.009 · Cited by 805
Friston, K. (2013) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0475 · Cited by 754
Datta, A., Sen, S., & Zick, Y. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.42 · Cited by 728
A wavelet-based continuous classification scheme for multifunction myoelectric control
Englehart, K., Hudgin, B., & Parker, P. A. (2001) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.914793 · Cited by 697
Stochastic processes: Time evolution, symmetries and linear response
Hänggi, P., & Thomas, H. (1982) · Physics Reports · doi:10.1016/0370-1573(82)90045-x · Cited by 659
Uncertainty and stress: Why it causes diseases and how it is mastered by the brain
Peters, A., McEwen, B. S., & Friston, K. (2017) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2017.05.004 · Cited by 652
“From a theoretical perspective, the difference between attainable states and goal states can be formalized by the so-called ‘Kullback Leibler (KL) divergence’ (Kullback and Leibler, 1951).”
Gaia Data Release 2: Catalogue validation
Arenou, F., Luri, X., Babusiaux, C., Fabricius, C., Helmi, A., Muraveva, T., Robin, A. C., Spoto, F., Vallenari, A., Antoja, T., et al. (2018) · Astronomy & Astrophysics · doi:10.1051/0004-6361/201833234 · Cited by 601
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
Bruzzone, L., & Marconcini, M. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.57 · Cited by 591
Shin, H.-C., Orton, M. R., Collins, D. J., Doran, S. J., & Leach, M. O. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.277 · Cited by 488
Bliege Bird, R., Bird, D. W., Codding, B. F., Parker, C. H., & Jones, J. H. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0804757105 · Cited by 494
Comparing hemodynamic models with DCM
Stephan, K. E., Weiskopf, N., Drysdale, P. M., Robinson, P. A., & Friston, K. J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.040 · Cited by 441
Nonlinear dynamic causal models for fMRI
Stephan, K. E., Kasper, L., Harrison, L. M., Daunizeau, J., den Ouden, H. E. M., Breakspear, M., & Friston, K. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.04.262 · Cited by 416
Henson, J. M., Reise, S. P., & Kim, K. H. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510709336744 · Cited by 429
A Closer Look at Invalid Action Masking in Policy Gradient Algorithms
Huang, S., & Ontañón, S. (2022) · The International FLAIRS Conference Proceedings · doi:10.32473/flairs.v35i.130584 · Cited by 435
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
A generative modeling approach for benchmarking and training shallow quantum circuits
Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V., Nam, Y., & Perdomo-Ortiz, A. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0157-8 · Cited by 379
A free energy principle for biological systems
Karl, F. (2012) · Entropy · doi:10.3390/e14112100 · Cited by 377
Elucidating Compound Mechanism of Action by Network Perturbation Analysis
Woo, J. H., Shimoni, Y., Yang, W. S., Subramaniam, P., Iyer, A., Nicoletti, P., Rodríguez Martínez, M., López, G., Mattioli, M., Realubit, R., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.056 · Cited by 351
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337
Sufficient subalgebras and the relative entropy of states of a von Neumann algebra
Petz, D. (1986) · Communications in Mathematical Physics · doi:10.1007/bf01212345 · Cited by 333
A Survey on Quantum Channel Capacities
Gyongyosi, L., Imre, S., & Nguyen, H. V. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2786748 · Cited by 337
Evaluating the Accuracy of Continuous Glucose-Monitoring Sensors
Kovatchev, B. P., Gonder-Frederick, L. A., Cox, D. J., & Clarke, W. L. (2004) · Diabetes Care · doi:10.2337/diacare.27.8.1922 · Cited by 312
Anatomical coupling between distinct metacognitive systems for memory and visual perception
McCurdy, L. Y., Maniscalco, B., Metcalfe, J., Liu, K. Y., de Lange, F. P., & Lau, H. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.1890-12.2013 · Cited by 299
Statistical binning enables an accurate coalescent-based estimation of the avian tree
Mirarab, S., Bayzid, M. S., Boussau, B., & Warnow, T. (2014) · Science · doi:10.1126/science.1250463 · Cited by 257
Differentiable learning of quantum circuit Born machines
Liu, J.-G., & Wang, L. (2018) · Physical Review A · doi:10.1103/physreva.98.062324 · Cited by 279
AITCHISON, J. (1975) · Biometrika · doi:10.1093/biomet/62.3.547 · Cited by 256
Knowing one's place: a free-energy approach to pattern regulation
Friston, K., Levin, M., Sengupta, B., & Pezzulo, G. (2015) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.1383 · Cited by 234
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229
Active inference on discrete state-spaces: A synthesis
Da Costa, L., Parr, T., Sajid, N., Veselic, S., Neacsu, V., & Friston, K. (2020) · Journal of Mathematical Psychology · doi:10.1016/j.jmp.2020.102447 · Cited by 226
“An alternative to computing the exact posterior distribution is to optimise an approximate posterior distribution over latent causes Q(s1:T , A, π), by minimising the Kullback-Leibler (KL) divergence [95] (DKL) – a non-negative measure of discrepancy between probability distributions.”
MethodKnowledge Distillation with the Reused Teacher Classifier
Chen, D., Mei, J.-P., Zhang, H., Wang, C., Feng, Y., & Chen, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01163 · Cited by 254
Kravchik, M., & Shabtai, A. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3050101 · Cited by 224
Maximum entropy autoregressive conditional heteroskedasticity model
Park, S. Y., & Bera, A. K. (2009) · Journal of Econometrics · doi:10.1016/j.jeconom.2008.12.014 · Cited by 218
Gaussian process robust regression for noisy heart rate data
Stegle, O., Fallert, S. V., MacKay, D. J. C., & Brage, S. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.923118 · Cited by 213
Grgic-Hlaca, N., Redmiles, E. M., Gummadi, K. P., & Weller, A. (2018) · Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18 · doi:10.1145/3178876.3186138 · Cited by 207
Organization of prefrontal network activity by respiration-related oscillations
Biskamp, J., Bartos, M., & Sauer, J.-F. (2017) · Scientific Reports · doi:10.1038/srep45508 · Cited by 197
Mining query logs: turning search usage data into knowledge
Silvestri, F. (2009) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000013 · Cited by 194
Missing data in multi-omics integration: Recent advances through artificial intelligence
Flores, J. E., Claborne, D. M., Weller, Z. D., Webb-Robertson, B.-J. M., Waters, K. M., & Bramer, L. M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1098308 · Cited by 181
Giantomassi, A., Ferracuti, F., Iarlori, S., Ippoliti, G., & Longhi, S. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2370936 · Cited by 168
Roux, F., Wibral, M., Singer, W., Aru, J., & Uhlhaas, P. J. (2013) · The Journal of Neuroscience · doi:10.1523/jneurosci.5778-12.2013 · Cited by 169
Patterns across multiple memories are identified over time
Richards, B. A., Xia, F., Santoro, A., Husse, J., Woodin, M. A., Josselyn, S. A., & Frankland, P. W. (2014) · Nature Neuroscience · doi:10.1038/nn.3736 · Cited by 150
Sun, S., & Xu, X. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2010.2093575 · Cited by 137
Markov blankets, information geometry and stochastic thermodynamics
Parr, T., Da Costa, L., & Friston, K. (2020) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2019.0159 · Cited by 141
Gruenerbl, A., Osmani, V., Bahle, G., Carrasco, J. C., Oehler, S., Mayora, O., Haring, C., & Lukowicz, P. (2014) · Proceedings of the 5th Augmented Human International Conference · doi:10.1145/2582051.2582089 · Cited by 139
Iterative algorithm for reconstruction of entangled states
Řeháček, J., Hradil, Z., & Ježek, M. (2001) · Physical Review A · doi:10.1103/physreva.63.040303 · Cited by 130
Improved measures of integrated information
Tegmark, M. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005123 · Cited by 128
An Appraisal of Incremental Learning Methods
Luo, Y., Yin, L., Bai, W., & Mao, K. (2020) · Entropy · doi:10.3390/e22111190 · Cited by 120
Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review
Brunner, V., Siegl, M., Geier, D., & Becker, T. (2021) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2021.722202 · Cited by 100
Qian, W., Li, S., & Wang, J. (2018) · IEEE Access · doi:10.1109/access.2018.2880770 · Cited by 91
What's color got to do with it? The influence of color on visual attention in different categories
Frey, H. P., Honey, C., & Konig, P. (2008) · Journal of Vision · doi:10.1167/8.14.6 · Cited by 91
Introducing a Bayesian model of selective attention based on active inference
Mirza, M. B., Adams, R. A., Friston, K., & Parr, T. (2019) · Scientific Reports · doi:10.1038/s41598-019-50138-8 · Cited by 83
Automatic bearing fault diagnosis using particle swarm clustering and hidden Markov model
Yuwono, M., Qin, Y., Zhou, J., Guo, Y., Celler, B. G., & Su, S. W. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2015.03.007 · Cited by 88
Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
Wu, L., Hu, D., Hong, L., & Liu, H. (2018) · The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval · doi:10.1145/3209978.3209993 · Cited by 85
Kharazmi, P., Kalia, S., Lui, H., Wang, Z. J., & Lee, T. K. (2018) · Skin Research and Technology · doi:10.1111/srt.12422 · Cited by 83
Quantifying the global parameter tensions between ACT, SPT, and Planck
Handley, W., & Lemos, P. (2021) · Physical Review D · doi:10.1103/physrevd.103.063529 · Cited by 77
Learning about the world through long-term query logs
Richardson, M. (2008) · ACM Transactions on the Web · doi:10.1145/1409220.1409224 · Cited by 77
A Bayesian Cognition Approach to Improve Data Visualization
Kim, Y.-S., Walls, L. A., Krafft, P., & Hullman, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300912 · Cited by 76
Active articulation model estimation through interactive perception
Hausman, K., Niekum, S., Osentoski, S., & Sukhatme, G. S. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7139655 · Cited by 77
Improved survival analysis by learning shared genomic information from pan-cancer data
Kim, S., Kim, K., Choe, J., Lee, I., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa462 · Cited by 77
Bowman, G. R. (2012) · The Journal of Chemical Physics · doi:10.1063/1.4755751 · Cited by 76
Sequence-to-conformation relationships of disordered regions tethered to folded domains of proteins
Mittal, A., Holehouse, A. S., Cohan, M. C., & Pappu, R. V. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.05.012 · Cited by 72
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers
Faraone, J., Kumm, M., Hardieck, M., Zipf, P., Liu, X., Boland, D., & Leong, P. H. W. (2020) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2019.2939429 · Cited by 66
Campillos-Llanos, L., Valverde-Mateos, A., Capllonch-Carrión, A., & Moreno-Sandoval, A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01395-z · 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
Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data
Abdelmoula, W. M., Regan, M. S., Lopez, B. G. C., Randall, E. C., Lawler, S., Mladek, A. C., Nowicki, M. O., Marin, B. M., Agar, J. N., Swanson, K. R., et al. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b00854 · Cited by 62
A Survey of Machine Unlearning
Nguyen, T. T., Huynh, T. T., Ren, Z., Nguyen, P. L., Liew, A. W.-C., Yin, H., & Nguyen, Q. V. H. (2022) · arXiv · doi:10.48550/arxiv.2209.02299 · Cited by 193
“5 ∗ KL(Td (x) | |m), whereM is unlearned model,Td is a competent teacher, and KL is The Kullback-Leibler divergence [84],m = M (x)+Td (x) 2 .”
Hullman, J., Kay, M., Kim, Y.-S., & Shrestha, S. (2018) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2743898 · Cited by 55
Linear and non-linear Modified Gravity forecasts with future surveys
Casas, S., Kunz, M., Martinelli, M., & Pettorino, V. (2017) · Physics of the Dark Universe · doi:10.1016/j.dark.2017.09.009 · Cited by 48
Tensions between direct measurements of the lens power spectrum from Planck data
Motloch, P., & Hu, W. (2018) · Physical Review D · doi:10.1103/physrevd.97.103536 · Cited by 46
Strength in Numbers: Robust Tamper Detection in Crowd Computations
Viswanath, B., Bashir, M. A., Zafar, M. B., Bouget, S., Guha, S., Gummadi, K. P., Kate, A., & Mislove, A. (2015) · Proceedings of the 2015 ACM on Conference on Online Social Networks · doi:10.1145/2817946.2817964 · Cited by 43
Global extreme heat forecasting using neural weather models
Lopez-Gomez, I., McGovern, A., Agrawal, S., & Hickey, J. (2023) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0035.1 · Cited by 41
Informational laws of genome structures
Bonnici, V., & Manca, V. (2016) · Scientific Reports · doi:10.1038/srep28840 · Cited by 38
Neural dynamics under active inference: plausibility and efficiency of information processing
Da Costa, L., Parr, T., Sengupta, B., & Friston, K. (2021) · Entropy · doi:10.3390/e23040454 · Cited by 38
ParSNIP: Generative Models of Transient Light Curves with Physics-enabled Deep Learning
Boone, K. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/ac2a2d · Cited by 38
“The second term represents the Kullback−Leibler divergence (Kullback & Leibler 1951) between the approximate posterior qf(s|y) and the prior pθ(s), and is effectively a constraint on the form of the approximate posterior.”
Background
Metadata
- Authors
- Kullback, S., & Leibler, R. A.
- Venue
- The Annals of Mathematical Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 1951-03
- Volume
- 22
- Issue
- 1
- Pages
- 79-86
- ISSN
- 0003-4851 (ISSN-L)
- OpenAlex
- W1965555277
- Cited by
- 20,193
- References
- 0
- Citations by year
- 2026: 1111 · 2025: 1372 · 2024: 1320 · 2023: 1325 · 2022: 1331 · 2021: 1488 · 2020: 1323 · 2019: 1162 · 2018: 941 · 2017: 856 · 2016: 799 · 2015: 784 · 2014: 799 · 2013: 733 · 2012: 661
- FWCI
- 8.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Mathematical Statistics (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- information, sufficiency, sufficient statistics, information theory, statistical inference
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
