Elements of Information Theory

Cell type-specific structural plasticity of axonal branches and boutons in the adult neocortex

De Paola, V., Holtmaat, A., Knott, G., Song, S., Wilbrecht, L., Caroni, P., & Svoboda, K. (2006) · Neuron · doi:10.1016/j.neuron.2006.02.017 · Cited by 419

Reverse engineering cellular networks

Margolin, A. A., Wang, K., Lim, W. K., Kustagi, M., Nemenman, I., & Califano, A. (2006) · Nature Protocols · doi:10.1038/nprot.2006.106 · Cited by 379

Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations

Agustsson, E., Mentzer, F., Tschannen, M., Cavigelli, L., Timofte, R., Benini, L., & Van Gool, L. (2017) · arXiv · doi:10.48550/arxiv.1704.00648 · Cited by 403

Mutual information analysis of the EEG in patients with Alzheimer's disease

Jeong, J., Gore, J. C., & Peterson, B. S. (2001) · Clinical Neurophysiology · doi:10.1016/s1388-2457(01)00513-2 · Cited by 397

Untitled work

(1999) · Cited by 397

Progress and challenges in the automated construction of Markov state models for full protein systems

Bowman, G. R., Beauchamp, K. A., Boxer, G., & Pande, V. S. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3216567 · Cited by 396

Influence of boundary removal on the spatial representations of the medial entorhinal cortex

Savelli, F., Yoganarasimha, D., & Knierim, J. J. (2008) · Hippocampus · doi:10.1002/hipo.20511 · Cited by 363

Toward a synthesis of cognitive biases: How noisy information processing can bias human decision making.

Hilbert, M. (2012) · Psychological Bulletin · doi:10.1037/a0025940 · Cited by 391

Deep Semi-Supervised Anomaly Detection

Ruff, L., Vandermeulen, R. A., Görnitz, N., Binder, A., Müller, E., Müller, K.-R., & Kloft, M. (2019) · arXiv · doi:10.48550/arxiv.1906.02694 · Cited by 388

Estimating Divergence Functionals and the Likelihood Ratio by Convex Risk Minimization

Nguyen, X., Wainwright, M. J., & Jordan, M. I. (2010) · IEEE Transactions on Information Theory · doi:10.1109/tit.2010.2068870 · Cited by 379

Quantifying causal emergence shows that macro can beat micro

Hoel, E. P., Albantakis, L., & Tononi, G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314922110 · Cited by 378

Universal prediction of individual sequences

Feder, M., Merhav, N., & Gutman, M. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.144706 · Cited by 373

Entropy measures for networks: Toward an information theory of complex topologies

Anand, K., & Bianconi, G. (2009) · Physical Review E · doi:10.1103/physreve.80.045102 · Cited by 368

Acoustic sequences in non‐human animals: a tutorial review and prospectus

Kershenbaum, A., Blumstein, D. T., Roch, M. A., Akçay, Ç., Backus, G., Bee, M. A., Bohn, K., Cao, Y., Carter, G., Cäsar, C., et al. (2016) · Biological Reviews · doi:10.1111/brv.12160 · Cited by 337

A Fully Quantum Asymptotic Equipartition Property

Tomamichel, M., Colbeck, R., & Renner, R. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2009.2032797 · Cited by 356

Single-cell chromatin modification profiling reveals increased epigenetic variations with aging

Cheung, P., Vallania, F., Warsinske, H. C., Donato, M., Schaffert, S., Chang, S. E., Dvorak, M., Dekker, C. L., Davis, M. M., Utz, P. J., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.079 · Cited by 323

Angular synchronization by eigenvectors and semidefinite programming

Singer, A. (2011) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2010.02.001 · Cited by 329

Efficient Greedy Learning of Gaussian Mixture Models

Verbeek, J. J., Vlassis, N., & Kröse, B. (2003) · Neural Computation · doi:10.1162/089976603762553004 · Cited by 340

Using deep sequencing to characterize the biophysical mechanism of a transcriptional regulatory sequence

Kinney, J. B., Murugan, A., Callan, C. G., & Cox, E. C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004290107 · Cited by 334

Quantum channels and memory effects

Caruso, F., Giovannetti, V., Lupo, C., & Mancini, S. (2012) · arXiv · doi:10.48550/arxiv.1207.5435 · Cited by 331

Applied Stochastic Differential Equations

Särkkä, S., & Solin, A. (2019) · doi:10.1017/9781108186735 · Cited by 326

Reduced-Reference Image Quality Assessment Using Divisive Normalization-Based Image Representation

Li, Q., & Wang, Z. (2009) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2009.2014497 · Cited by 323

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

Learning words from sights and sounds: A computational model

Roy, D. K., & Pentland, A. P. (2002) · Cognitive Science · doi:10.1207/s15516709cog2601_4 · Cited by 521

Experimental validation of photonic boson sampling

Spagnolo, N., Vitelli, C., Bentivegna, M., Brod, D. J., Crespi, A., Flamini, F., Giacomini, S., Milani, G., Ramponi, R., Mataloni, P., et al. (2014) · Nature Photonics · doi:10.1038/nphoton.2014.135 · Cited by 312

The Interface Theory of Perception

Hoffman, D. D., Singh, M., & Prakash, C. (2015) · Psychonomic Bulletin & Review · doi:10.3758/s13423-015-0890-8 · Cited by 329

A neural correlate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision task

Haruno, M., Kuroda, T., Doya, K., Toyama, K., Kimura, M., Samejima, K., Imamizu, H., & Kawato, M. (2004) · The Journal of Neuroscience · doi:10.1523/jneurosci.3417-03.2004 · Cited by 300

Big privacy: challenges and opportunities of privacy study in the age of big data

Yu, S. (2016) · IEEE Access · doi:10.1109/access.2016.2577036 · Cited by 307

Coding for computing

Orlitsky, A., & Roche, J. R. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.915643 · Cited by 307

A New Proof of the Quantum Noiseless Coding Theorem

Jozsa, R., & Schumacher, B. (1994) · Journal of Modern Optics · doi:10.1080/09500349414552191 · Cited by 304

Measuring information integration

Tononi, G., & Sporns, O. (2003) · BMC Neuroscience · doi:10.1186/1471-2202-4-31 · Cited by 294

Information retrieval and text mining technologies for chemistry

Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281

Quality-aware images

Wang, Z., Wu, G., Sheikh, H. R., Simoncelli, E. P., Yang, E.-H., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.864165 · Cited by 293

Reduced- and No-Reference Image Quality Assessment

Wang, Z., & Bovik, A. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.942471 · Cited by 280

UVeQFed: Universal Vector Quantization for Federated Learning

Shlezinger, N., Chen, M., Eldar, Y. C., Poor, H. V., & Cui, S. (2021) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.3046971 · Cited by 275

Information-Theoretic Inference of Large Transcriptional Regulatory Networks

Meyer, P. E., Kontos, K., Lafitte, F., & Bontempi, G. (2007) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1155/2007/79879 · Cited by 273

Bell inequality, Bell states and maximally entangled states for n qubits

Gisin, N., & Bechmann-Pasquinucci, H. (1998) · Physics Letters A · doi:10.1016/s0375-9601(98)00516-7 · Cited by 270

Qualia: The Geometry of Integrated Information

Balduzzi, D., & Tononi, G. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000462 · Cited by 269

Coding of temporal information by activity-dependent synapses.

Fuhrmann, G., Segev, I., Markram, H., & Tsodyks, M. (2002) · Journal of Neurophysiology · doi:10.1152/jn.00258.2001 · Cited by 267

Information Theory of Decisions and Actions

Tishby, N., & Polani, D. (2011) · Perception-Action Cycle · doi:10.1007/978-1-4419-1452-1_19 · Cited by 267

TimeDelay-ARACNE: reverse engineering of gene networks from time-course data by an information theoretic approach

Zoppoli, P., Morganella, S., & Ceccarelli, M. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-154 · Cited by 248

Unifying Count-Based Exploration and Intrinsic Motivation

Bellemare, M. G., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D., & Munos, R. (2016) · arXiv · doi:10.48550/arxiv.1606.01868 · Cited by 264

Optimized transmission of JPEG2000 streams over wireless channels

Thomos, N., Boulgouris, N. V., & Strintzis, M. G. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.860338 · Cited by 258

Predictive information in a sensory population

Palmer, S. E., Marre, O., Berry, M. J., & Bialek, W. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1506855112 · Cited by 257

Maximum entropy sampling and optimal bayesian experimental design

Sebastiani, P., & Wynn, H. P. (2000) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00225 · Cited by 243

An information-theoretic primer on complexity, self-organization, and emergence

Prokopenko, M., Boschetti, F., & Ryan, A. J. (2009) · Complexity · doi:10.1002/cplx.20249 · Cited by 251

Beyond Bell's theorem: correlation scenarios

Fritz, T. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/10/103001 · Cited by 243

Mining StackOverflow to turn the IDE into a self-confident programming prompter

Ponzanelli, L., Bavota, G., Di Penta, M., Oliveto, R., & Lanza, M. (2014) · Proceedings of the 11th Working Conference on Mining Software Repositories · doi:10.1145/2597073.2597077 · Cited by 240

Reverse engineering and identification in systems biology: strategies, perspectives and challenges

Villaverde, A. F., & Banga, J. R. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2013.0505 · Cited by 229

Experimental scattershot boson sampling

Bentivegna, M., Spagnolo, N., Vitelli, C., Flamini, F., Viggianiello, N., Latmiral, L., Mataloni, P., Brod, D. J., Galvão, E. F., Crespi, A., et al. (2015) · Science Advances · doi:10.1126/sciadv.1400255 · Cited by 239

Eviza: A Natural Language Interface for Visual Analysis

Setlur, V., Battersby, S. E., Tory, M., Gossweiler, R., & Chang, A. X. (2016) · Proceedings of the 29th Annual Symposium on User Interface Software and Technology · doi:10.1145/2984511.2984588 · Cited by 231

Information processing and the second law of thermodynamics: an inclusive, Hamiltonian approach

Deffner, S., & Jarzynski, C. (2013) · Physical Review X · doi:10.1103/physrevx.3.041003 · Cited by 231

The Nonlinear Statistics of High-Contrast Patches in Natural Images

Lee, A. B., Pedersen, K. S., & Mumford, D. (2003) · International Journal of Computer Vision · doi:10.1023/a:1023705401078 · Cited by 234

Practical Measures of Integrated Information for Time-Series Data

Barrett, A. B., & Seth, A. K. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001052 · Cited by 225

No extension of quantum theory can have improved predictive power

Colbeck, R., & Renner, R. (2011) · Nature Communications · doi:10.1038/ncomms1416 · Cited by 223

The Cramer Distance as a Solution to Biased Wasserstein Gradients

Bellemare, M. G., Danihelka, I., Dabney, W., Mohamed, S., Lakshminarayanan, B., Hoyer, S., & Munos, R. (2017) · arXiv · doi:10.48550/arxiv.1705.10743 · Cited by 223

Higher-Order Networks

Bianconi, G. (2021) · doi:10.1017/9781108770996 · Cited by 223

The Universal Composable Security of Quantum Key Distribution

Ben-Or, M., Horodecki, M., Leung, D. W., Mayers, D., & Oppenheim, J. (2005) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-30576-7_21 · Cited by 227

On Stopwords, Filtering and Data Sparsity for Sentiment Analysis of Twitter

Saif, H., Fernandez, M., He, Y., & Alani, H. (2014) · Proceedings of the Language Resources and Evaluation Conference · doi:10.63317/3uepwmfe264a · Cited by 219

General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

Molnar, C., König, G., Herbinger, J., Freiesleben, T., Dandl, S., Scholbeck, C. A., Casalicchio, G., Grosse-Wentrup, M., & Bischl, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-04083-2_4 · Cited by 211

A Direct Brain-to-Brain Interface in Humans

Rao, R. P. N., Stocco, A., Bryan, M., Sarma, D., Youngquist, T. M., Wu, J., & Prat, C. S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111332 · Cited by 210

A review of gaze entropy as a measure of visual scanning efficiency

Shiferaw, B., Downey, L., & Crewther, D. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.12.007 · Cited by 203

A formal analysis of cytokine networks in chronic fatigue syndrome

Broderick, G., Fuite, J., Kreitz, A., Vernon, S. D., Klimas, N., & Fletcher, M. A. (2010) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2010.04.012 · Cited by 202

Detecting Cars in UAV Images With a Catalog-Based Approach

Moranduzzo, T., & Melgani, F. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2296351 · Cited by 200

Unexpected means of protocol inference

Ma, J., Levchenko, K., Kreibich, C., Savage, S., & Voelker, G. M. (2006) · Proceedings of the 6th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1177080.1177123 · Cited by 204

Energy–time uncertainty relation for driven quantum systems

Deffner, S., & Lutz, E. (2013) · Journal of Physics A: Mathematical and Theoretical · doi:10.1088/1751-8113/46/33/335302 · Cited by 199

Measuring Integrated Information from the Decoding Perspective

Oizumi, M., Amari, S.-I., Yanagawa, T., Fujii, N., & Tsuchiya, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004654 · Cited by 184

Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff

Blau, Y., & Michaeli, T. (2019) · arXiv · doi:10.48550/arxiv.1901.07821 · Cited by 198

On Issues of Instance Selection

Liu, H., & Motoda, H. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014056429969 · Cited by 191

Fast HEVC Encoding Decisions Using Data Mining

Correa, G., Assuncao, P. A., Agostini, L. V., & da Silva Cruz, L. A. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2014.2363753 · Cited by 190

Inferring Causal Gene Regulatory Networks from Coupled Single-Cell Expression Dynamics Using Scribe

Qiu, X., Rahimzamani, A., Wang, L., Ren, B., Mao, Q., Durham, T., McFaline-Figueroa, J. L., Saunders, L., Trapnell, C., & Kannan, S. (2020) · Cell Systems · doi:10.1016/j.cels.2020.02.003 · Cited by 185

A proof of the Fisher information inequality via a data processing argument

Zamir, R. (1998) · IEEE Transactions on Information Theory · doi:10.1109/18.669301 · Cited by 171

A novel image encryption scheme based on an elliptic curve

Hayat, U., & Azam, N. A. (2019) · Signal Processing · doi:10.1016/j.sigpro.2018.10.011 · Cited by 178

Resource-rational decision making

Bhui, R., Lai, L., & Gershman, S. J. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2021.02.015 · Cited by 168

Making AI Forget You: Data Deletion in Machine Learning

Ginart, A., Guan, M. Y., Valiant, G., & Zou, J. (2019) · arXiv · doi:10.48550/arxiv.1907.05012 · Cited by 175

What HMMs Can Do

BILMES, J. A. (2006) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e89-d.3.869 · Cited by 172

Security of practical private randomness generation

Pironio, S., & Massar, S. (2013) · Physical Review A · doi:10.1103/physreva.87.012336 · Cited by 169

Optogenetic dissection of descending behavioral control in Drosophila

Cande, J., Namiki, S., Qiu, J., Korff, W., Card, G. M., Shaevitz, J. W., Stern, D. L., & Berman, G. J. (2018) · eLife · doi:10.7554/elife.34275 · Cited by 163

Valid population inference for information-based imaging: From the second-level t -test to prevalence inference

Allefeld, C., Görgen, K., & Haynes, J.-D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.040 · Cited by 157

Variational Bayes for generalized autoregressive models

Roberts, S. J., & Penny, W. D. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.801921 · Cited by 157

Reinforcement learning and decision making in monkeys during a competitive game

Lee, D., Conroy, M. L., McGreevy, B. P., & Barraclough, D. J. (2004) · Cognitive Brain Research · doi:10.1016/j.cogbrainres.2004.07.007 · Cited by 156

An exact method to quantify the information transmitted by different mechanisms of correlational coding

Pola, G., Thiele, A., Hoffmann, K.-P., & Panzeri, S. (2003) · Network: Computation in Neural Systems · doi:10.1088/0954-898x/14/1/303 · Cited by 151

Decoherence, chaos, quantum-classical correspondence, and the algorithmic arrow of time

Zurek, W. H. (1998) · Physica Scripta · doi:10.1238/physica.topical.076a00186 · Cited by 150

Generalized hidden Markov models for phylogenetic comparative datasets

Boyko, J. D., & Beaulieu, J. M. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13534 · Cited by 146

The Quantum Theory of Optical Communications

Shapiro, J. H. (2009) · IEEE Journal of Selected Topics in Quantum Electronics · doi:10.1109/jstqe.2009.2024959 · Cited by 145

Approximate Quantum Error Correction

Schumacher, B., & Westmoreland, M. D. (2002) · Quantum Information Processing · doi:10.1023/a:1019653202562 · Cited by 142

Some Comparisons Among Quadratic, Spherical, and Logarithmic Scoring Rules

Bickel, J. E. (2007) · Decision Analysis · doi:10.1287/deca.1070.0089 · Cited by 142

A Theory of Usable Information Under Computational Constraints

Xu, Y., Zhao, S., Song, J., Stewart, R., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2002.10689 · Cited by 142

How Programmers Debug, Revisited: An Information Foraging Theory Perspective

Lawrance, J., Bogart, C., Burnett, M., Bellamy, R., Rector, K., & Fleming, S. D. (2013) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2010.111 · Cited by 142

Data-Free Network Quantization With Adversarial Knowledge Distillation

Choi, Y., Choi, J., El-Khamy, M., & Lee, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00363 · Cited by 135

Quantum Entanglement and the Communication Complexity of the Inner Product Function

Cleve, R., van Dam, W., Nielsen, M., & Tapp, A. (1999) · Lecture Notes in Computer Science · doi:10.1007/3-540-49208-9_4 · Cited by 133

Auditory Cortical Detection and Discrimination Correlates with Communicative Significance

Liu, R. C., & Schreiner, C. E. (2007) · PLoS Biology · doi:10.1371/journal.pbio.0050173 · Cited by 130

Explainable Object-Induced Action Decision for Autonomous Vehicles

Xu, Y., Yang, X., Gong, L., Lin, H.-C., Wu, T.-Y., Li, Y., & Vasconcelos, N. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00954 · Cited by 129

Clinical text classification research trends: Systematic literature review and open issues

Mujtaba, G., Shuib, L., Idris, N., Hoo, W. L., Raj, R. G., Khowaja, K., Shaikh, K., & Nweke, H. F. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.034 · Cited by 123

A Bayesian approach to determining connectivity of the human brain

Patel, R. S., Bowman, F. D., & Rilling, J. K. (2006) · Human Brain Mapping · doi:10.1002/hbm.20182 · Cited by 127

Correlated mutations in models of protein sequences: phylogenetic and structural effects

Lapedes, A. S., Giraud, B., Liu, L., & Stormo, G. D. (1999) · Institute of Mathematical Statistics Lecture Notes - Monograph Series · doi:10.1214/lnms/1215455556 · Cited by 124

Low-Depth Gradient Measurements Can Improve Convergence in Variational Hybrid Quantum-Classical Algorithms

Harrow, A. W., & Napp, J. C. (2021) · Physical Review Letters · doi:10.1103/physrevlett.126.140502 · Cited by 121

2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators

Zollei, L., Grimson, E., Norbash, A., & Wells, W. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.991032 · Cited by 120

Reliable support: measuring calibration of likelihood ratios

Ramos, D., & Gonzalez-Rodriguez, J. (2013) · Forensic Science International · doi:10.1016/j.forsciint.2013.04.014 · Cited by 118

Deep Deterministic Uncertainty: A New Simple Baseline

Mukhoti, J., Kirsch, A., van Amersfoort, J., Torr, P. H. S., & Gal, Y. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02336 · Cited by 116