Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference

Community detection in graphs

Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293

The graph neural network model

Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2009) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2008.2005605 · Cited by 9,582

The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function

Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., Franz, M., Grouios, C., Kazi, F., Lopes, C. T., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq537 · Cited by 4,835

An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance

Aston-Jones, G., & Cohen, J. D. (2005) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.28.061604.135709 · Cited by 4,472

Survey of Hallucination in Natural Language Generation

Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025

Collective Classification in Network Data

Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T. (2008) · AI Magazine · doi:10.1609/aimag.v29i3.2157 · Cited by 3,293

Reservoir computing approaches to recurrent neural network training

Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956

A survey on semi-supervised learning

van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666

Critical phenomena in complex networks

Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F. (2008) · Reviews of Modern Physics · doi:10.1103/revmodphys.80.1275 · Cited by 2,220

Recent advances in physical reservoir computing: A review

Tanaka, G., Yamane, T., Héroux, J. B., Nakane, R., Kanazawa, N., Takeda, S., Numata, H., Nakano, D., & Hirose, A. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.03.005 · Cited by 2,146

The hippocampal-VTA loop: controlling the entry of information into long-term memory

Lisman, J. E., & Grace, A. A. (2005) · Neuron · doi:10.1016/j.neuron.2005.05.002 · Cited by 1,983

Foundation models for generalist medical artificial intelligence

Moor, M., Banerjee, O., Abad, Z. S. H., Krumholz, H. M., Leskovec, J., Topol, E. J., & Rajpurkar, P. (2023) · Nature · doi:10.1038/s41586-023-05881-4 · Cited by 2,028

Uncertainty, Neuromodulation, and Attention

Yu, A. J., & Dayan, P. (2005) · Neuron · doi:10.1016/j.neuron.2005.04.026 · Cited by 1,774

Habits, Rituals, and the Evaluative Brain

Graybiel, A. M. (2008) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112851 · Cited by 1,697

Optimality principles in sensorimotor control

Todorov, E. (2004) · Nature Neuroscience · doi:10.1038/nn1309 · Cited by 1,746

Convex and Semi-Nonnegative Matrix Factorizations

Ding, C. H. Q., Li, T., & Jordan, M. I. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.277 · Cited by 1,317

Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach

Norman, K. A., & O'Reilly, R. C. (2003) · Psychological Review · doi:10.1037/0033-295x.110.4.611 · Cited by 1,312

Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation

Fouss, F., Pirotte, A., Renders, J.-M., & Saerens, M. (2007) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2007.46 · Cited by 1,265

Semantic hashing

Salakhutdinov, R., & Hinton, G. (2009) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2008.11.006 · Cited by 1,246

Single-trial analysis and classification of ERP components — A tutorial

Blankertz, B., Lemm, S., Treder, M., Haufe, S., & Müller, K.-R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.06.048 · Cited by 1,138

An experimental unification of reservoir computing methods

Verstraeten, D., Schrauwen, B., D’Haene, M., & Stroobandt, D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.04.003 · Cited by 1,135

Generating Coherent Patterns of Activity from Chaotic Neural Networks

Sussillo, D., & Abbott, L. F. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.018 · Cited by 1,092

Performance of modularity maximization in practical contexts

Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937

A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals

Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007) · Journal of Neural Engineering · doi:10.1088/1741-2560/4/2/r03 · Cited by 907

RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference

Maples, B. K., Gravel, S., Kenny, E. E., & Bustamante, C. D. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.06.020 · Cited by 900

A survey of multi-view machine learning

Sun, S. (2013) · Neural Computing and Applications · doi:10.1007/s00521-013-1362-6 · Cited by 878

Machine learning in bioinformatics

Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J. A., Armañanzas, R., Santafé, G., Pérez, A., et al. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbk007 · Cited by 867

Reward, Motivation, and Reinforcement Learning

Dayan, P., & Balleine, B. W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00963-7 · Cited by 855

Reinforcement learning of motor skills with policy gradients

Peters, J., & Schaal, S. (2008) · Neural Networks · doi:10.1016/j.neunet.2008.02.003 · Cited by 831

Social big data: recent achievements and new challenges

Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016) · Information Fusion · doi:10.1016/j.inffus.2015.08.005 · Cited by 846

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849

Human insula activation reflects risk prediction errors as well as risk

Preuschoff, K., Quartz, S. R., & Bossaerts, P. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4286-07.2008 · Cited by 784

Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces

Cecotti, H., & Graser, A. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.125 · Cited by 778

Automated reverse engineering of nonlinear dynamical systems

Bongard, J., & Lipson, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609476104 · Cited by 792

Active inference and epistemic value

Friston, K., Rigoli, F., Ognibene, D., Mathys, C., Fitzgerald, T., & Pezzulo, G. (2015) · Cognitive Neuroscience · doi:10.1080/17588928.2015.1020053 · Cited by 731

Three systems of insular functional connectivity identified with cluster analysis

Deen, B., Pitskel, N. B., & Pelphrey, K. A. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq186 · Cited by 716

Metalearning and neuromodulation

Doya, K. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00044-8 · Cited by 704

Could information theory provide an ecological theory of sensory processing?

Atick, J. J. (2011) · Network: Computation in Neural Systems · doi:10.3109/0954898x.2011.638888 · Cited by 679

Fractional differentiation by neocortical pyramidal neurons

Lundstrom, B. N., Higgs, M. H., Spain, W. J., & Fairhall, A. L. (2008) · Nature Neuroscience · doi:10.1038/nn.2212 · Cited by 678

Causal Models: How People Think about the World and Its Alternatives

Sloman, S. (2005) · doi:10.1093/acprof:oso/9780195183115.001.0001 · Cited by 677

Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data

Meisner, J., & Albrechtsen, A. (2018) · Genetics · doi:10.1534/genetics.118.301336 · Cited by 673

Neural Network Methods for Natural Language Processing

Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821

Parameter estimation for differential equations: a generalized smoothing approach

Ramsay, J. O., Hooker, G., Campbell, D., & Cao, J. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00610.x · Cited by 613

Twenty Years of Mixture of Experts

Yuksel, S. E., Wilson, J. N., & Gader, P. D. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2200299 · Cited by 606

How to train your robot with deep reinforcement learning: Lessons we have learned

Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., & Levine, S. (2021) · The International Journal of Robotics Research · doi:10.1177/0278364920987859 · Cited by 570

Age Synthesis and Estimation via Faces: A Survey

Fu, Y., Guo, G., & Huang, T. S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.36 · Cited by 569

Wishbone identifies bifurcating developmental trajectories from single-cell data

Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558

Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes

Dentro, S. C., Leshchiner, I., Haase, K., Tarabichi, M., Wintersinger, J., Deshwar, A. G., Yu, K., Rubanova, Y., Macintyre, G., Demeulemeester, J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.03.009 · Cited by 533

Building a knowledge graph to enable precision medicine

Chandak, P., Huang, K., & Zitnik, M. (2023) · Scientific Data · doi:10.1038/s41597-023-01960-3 · Cited by 522

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model

Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471

Adipose tissue transcriptomic signature highlights the pathological relevance of extracellular matrix in human obesity

Henegar, C., Tordjman, J., Achard, V., Lacasa, D., Cremer, I., Guerre-Millo, M., Poitou, C., Basdevant, A., Stich, V., Viguerie, N., et al. (2008) · Genome Biology · doi:10.1186/gb-2008-9-1-r14 · Cited by 471

Evidence for the Flexible Sensorimotor Strategies Predicted by Optimal Feedback Control

Liu, D., & Todorov, E. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1110-06.2007 · Cited by 447

Multiple instance learning for histopathological breast cancer image classification

Sudharshan, P. J., Petitjean, C., Spanhol, F., Oliveira, L. E., Heutte, L., & Honeine, P. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.09.049 · Cited by 369

Rhythmic arm movement is not discrete

Schaal, S., Sternad, D., Osu, R., & Kawato, M. (2004) · Nature Neuroscience · doi:10.1038/nn1322 · Cited by 368

Conditional random fields for activity recognition

Vail, D. L., Veloso, M. M., & Lafferty, J. D. (2007) · Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems · doi:10.1145/1329125.1329409 · Cited by 357

Rethinking extinction

Dunsmoor, J. E., Niv, Y., Daw, N., & Phelps, E. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.028 · Cited by 329

A systematic review of Green AI

Verdecchia, R., Sallou, J., & Cruz, L. (2023) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1507 · Cited by 375

Low-dimensional, free-energy landscapes of protein-folding reactions by nonlinear dimensionality reduction

Das, P., Moll, M., Stamati, H., Kavraki, L. E., & Clementi, C. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0603553103 · Cited by 331

Machine learning algorithms for construction projects delay risk prediction

Gondia, A., Siam, A., El-Dakhakhni, W., & Nassar, A. H. (2020) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001736 · Cited by 327

Habits, action sequences and reinforcement learning

Dezfouli, A., & Balleine, B. W. (2012) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2012.08050.x · Cited by 297

Pre-trained convolutional neural networks as feature extractors for tuberculosis detection

Lopes, U. K., & Valiati, J. F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.001 · Cited by 294

Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics

Harris, K. D., Hochgerner, H., Skene, N. G., Magno, L., Katona, L., Bengtsson Gonzales, C., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling-Leffler, J. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006387 · Cited by 276

SALMON: survival analysis learning with multi-omics neural networks on breast cancer

Huang, Z., Zhan, X., Xiang, S., Johnson, T. S., Helm, B., Yu, C. Y., Zhang, J., Salama, P., Rizkalla, M., Han, Z., et al. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00166 · Cited by 262

Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review

Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267

Dynamic movement primitives in robotics: A tutorial survey

Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · Cited by 267

Dynamics systems vs. optimal control — a unifying view

Schaal, S., Mohajerian, P., & Ijspeert, A. (2007) · Progress in Brain Research · doi:10.1016/s0079-6123(06)65027-9 · Cited by 260

Deep metric learning for few-shot image classification: A Review of recent developments

Li, X., Yang, X., Ma, Z., & Xue, J.-H. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109381 · Cited by 243

An Examination of the Use of Large Language Models to Aid Analysis of Textual Data

(2024) · doi:10.1177/16094069241231168 · Cited by 227

Perceptual video quality assessment: a survey

Min, X., Duan, H., Sun, W., Zhu, Y., & Zhai, G. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4133-3 · Cited by 218

Structure learning in action

Braun, D. A., Mehring, C., & Wolpert, D. M. (2010) · Behavioural Brain Research · doi:10.1016/j.bbr.2009.08.031 · Cited by 210

Perceptual Decision-Making as Probabilistic Inference by Neural Sampling

Haefner, R. M., Berkes, P., & Fiser, J. (2016) · Neuron · doi:10.1016/j.neuron.2016.03.020 · Cited by 207

Computational Health Informatics in the Big Data Age: A Survey

Fang, R., Pouyanfar, S., Yang, Y., Chen, S.-C., & Iyengar, S. S. (2017) · ACM Computing Surveys · doi:10.1145/2932707 · Cited by 210

A fully on-line adaptive BCI

Vidaurre, C., Schloogl, A., Cabeza, R., Scherer, R., & Pfurtscheller, G. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.873542 · Cited by 195

Entropy and information in neural spike trains: Progress on the sampling problem

Nemenman, I., Bialek, W., & de Ruyter van Steveninck, R. (2004) · Physical Review E · doi:10.1103/physreve.69.056111 · Cited by 196

Unleashing the power of generative AI in drug discovery

Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195

Anticipating and Addressing the Ethical Implications of Deepfakes in the Context of Elections

Diakopoulos, N., & Johnson, D. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3474183 · Cited by 194

3D shape perception from combined depth cues in human visual cortex

Welchman, A. E., Deubelius, A., Conrad, V., Bülthoff, H. H., & Kourtzi, Z. (2005) · Nature Neuroscience · doi:10.1038/nn1461 · Cited by 183

Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI

Tomsett, R., Preece, A., Braines, D., Cerutti, F., Chakraborty, S., Srivastava, M., Pearson, G., & Kaplan, L. (2020) · Patterns · doi:10.1016/j.patter.2020.100049 · Cited by 186

The AI Ghostwriter Effect: When Users do not Perceive Ownership of AI-Generated Text but Self-Declare as Authors

Draxler, F., Werner, A., Lehmann, F., Hoppe, M., Schmidt, A., Buschek, D., & Welsch, R. (2024) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3637875 · Cited by 186

Personalized Prompt Learning for Explainable Recommendation

Li, L., Zhang, Y., & Chen, L. (2023) · ACM Transactions on Information Systems · doi:10.1145/3580488 · Cited by 172

Systematic Fluctuation Expansion for Neural Network Activity Equations

Buice, M. A., Cowan, J. D., & Chow, C. C. (2010) · Neural Computation · doi:10.1162/neco.2009.02-09-960 · Cited by 164

Exploring a latent cause theory of classical conditioning

Gershman, S. J., & Niv, Y. (2012) · Learning & Behavior · doi:10.3758/s13420-012-0080-8 · Cited by 149

Low Dimensionality in Gene Expression Data Enables the Accurate Extraction of Transcriptional Programs from Shallow Sequencing

Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156

Attention and amplification in the hybrid media system: The composition and activity of Donald Trump’s Twitter following during the 2016 presidential election

Zhang, Y., Wells, C., Wang, S., & Rohe, K. (2018) · New Media & Society · doi:10.1177/1461444817744390 · Cited by 155

Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution

Srivastava, S., Vargas-Muñoz, J. E., & Tuia, D. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.04.014 · Cited by 151

Multistability and perceptual inference

Gershman, S. J., Vul, E., & Tenenbaum, J. B. (2012) · Neural Computation · doi:10.1162/neco_a_00226 · Cited by 140

The effects of day-to-day variability of physiological data on operator functional state classification

Christensen, J. C., Estepp, J. R., Wilson, G. F., & Russell, C. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.091 · Cited by 144

Untitled work

(2021) · UCL Discovery (University College London) · Cited by 141

Large language models show human-like content biases in transmission chain experiments

Acerbi, A., & Stubbersfield, J. M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313790120 · Cited by 137

Adding prediction risk to the theory of reward learning

PREUSCHOFF, K., & BOSSAERTS, P. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1390.005 · Cited by 133

Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep

Mitra, A., Snyder, A. Z., Tagliazucchi, E., Laufs, H., & Raichle, M. E. (2015) · eLife · doi:10.7554/elife.10781 · Cited by 122

The life cycle of large language models in education: A framework for understanding sources of bias

Lee, J., Hicke, Y., Yu, R., Brooks, C., & Kizilcec, R. F. (2024) · British Journal of Educational Technology · doi:10.1111/bjet.13505 · Cited by 119

Mixing up contrastive learning: Self-supervised representation learning for time series

Wickstrøm, K., Kampffmeyer, M., Mikalsen, K. Ø., & Jenssen, R. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.02.007 · Cited by 113

Large language models for chemistry robotics

Yoshikawa, N., Skreta, M., Darvish, K., Arellano-Rubach, S., Ji, Z., Bjørn Kristensen, L., Li, A. Z., Zhao, Y., Xu, H., Kuramshin, A., et al. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10136-2 · Cited by 112

Network planning with deep reinforcement learning

Zhu, H., Gupta, V., Ahuja, S. S., Tian, Y., Zhang, Y., & Jin, X. (2021) · Proceedings of the 2021 ACM SIGCOMM 2021 Conference · doi:10.1145/3452296.3472902 · Cited by 112

Gödel Machines: Fully Self-referential Optimal Universal Self-improvers

Schmidhuber, J. (2007) · Cognitive Technologies · doi:10.1007/978-3-540-68677-4_7 · Cited by 110

Masked inverse folding with sequence transfer for protein representation learning

(2022) · doi:10.1093/protein/gzad015 · Cited by 110

Efficient duration and hierarchical modeling for human activity recognition

Duong, T., Phung, D., Bui, H., & Venkatesh, S. (2009) · Artificial Intelligence · doi:10.1016/j.artint.2008.12.005 · Cited by 115

Deepfake Detection: A Comprehensive Survey from the Reliability Perspective

Wang, T., Liao, X., Chow, K. P., Lin, X., & Wang, Y. (2025) · ACM Computing Surveys · doi:10.1145/3699710 · Cited by 107

Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces

Dangi, S., Orsborn, A. L., Moorman, H. G., & Carmena, J. M. (2013) · Neural Computation · doi:10.1162/neco_a_00460 · Cited by 103