Independent Component Analysis

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001

An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.

Daubechies, I., Defrise, M., & De Mol, C. (2004) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20042 · Cited by 4,958

Estimating mutual information

Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 3,512

Neural correlations, population coding and computation

Averbeck, B. B., Latham, P. E., & Pouget, A. (2006) · Nature reviews. Neuroscience · doi:10.1038/nrn1888 · Cited by 1,743

Identification of slow molecular order parameters for Markov model construction

Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990

Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: From single‐subject to cortically aligned group general linear model analysis and self‐organizing group independent component analysis

Goebel, R., Esposito, F., & Formisano, E. (2006) · Human Brain Mapping · doi:10.1002/hbm.20249 · Cited by 1,053

Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art

Ghamisi, P., Yokoya, N., Li, J., Liao, W., Liu, S., Plaza, J., Rasti, B., & Plaza, A. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762087 · Cited by 909

Fairness-Aware Classifier with Prejudice Remover Regularizer

Kamishima, T., Akaho, S., Asoh, H., & Sakuma, J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33486-3_3 · Cited by 694

Cognitive computational neuroscience

Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535

xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain–Computer Interface

Rivet, B., Souloumiac, A., Attina, V., & Gibert, G. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2012869 · Cited by 537

Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric.

Holobar, A., Minetto, M. A., & Farina, D. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/1/016008 · Cited by 400

Causal discovery and inference: concepts and recent methodological advances

Spirtes, P., & Zhang, K. (2016) · Applied Informatics · doi:10.1186/s40535-016-0018-x · Cited by 304

Support vector machines in engineering: an overview

Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., & Camps‐Valls, G. (2014) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1125 · Cited by 301

Soil Diversity and Hydration as Observed by ChemCam at Gale Crater, Mars

Meslin, P.-Y., Gasnault, O., Forni, O., Schröder, S., Cousin, A., Berger, G., Clegg, S. M., Lasue, J., Maurice, S., Sautter, V., et al. (2013) · Science · doi:10.1126/science.1238670 · Cited by 268

Functional, But Not Anatomical, Separation of “What” and “When” in Prefrontal Cortex

Machens, C. K., Romo, R., & Brody, C. D. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3276-09.2010 · Cited by 260

Fast automated analysis of strong gravitational lenses with convolutional neural networks

Hezaveh, Y. D., Levasseur, L. P., & Marshall, P. J. (2017) · Nature · doi:10.1038/nature23463 · Cited by 261

D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery

Vowels, M. J., Camgoz, N. C., & Bowden, R. (2023) · ACM Computing Surveys · doi:10.1145/3527154 · Cited by 237

Structured Variability of Muscle Activations Supports the Minimal Intervention Principle of Motor Control

Valero-Cuevas, F. J., Venkadesan, M., & Todorov, E. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90324.2008 · Cited by 218

Complete Dictionary Recovery Over the Sphere I: Overview and the Geometric Picture

Sun, J., Qu, Q., & Wright, J. (2017) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2632162 · Cited by 213

Non-Invasive Functional-Brain-Imaging with an OPM-based Magnetoencephalography System

Borna, A., Carter, T. R., Colombo, A. P., Jau, Y.-Y., McKay, J., Weisend, M., Taulu, S., Stephen, J. M., & Schwindt, P. D. D. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227684 · Cited by 158

Watching the fetal brain at ‘rest’

Schöpf, V., Kasprian, G., Brugger, P. C., & Prayer, D. (2012) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2011.10.006 · Cited by 137

Passive Sonar Signal Detection and Classification Based on Independent Component Analysis

de Moura, N. N., de Seixas, J. M., & Ramos, R. (2011) · Sonar Systems · doi:10.5772/18286 · Cited by 37

Extraction of Sources of Tremor in Hand Movements of Patients With Movement Disorders

Vinjamuri, R., Crammond, D. J., Kondziolka, D., Lee, H.-N., & Mao, Z.-H. (2009) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.2006403 · Cited by 23

Unsupervised adaptation to improve fault tolerance of neural network classifiers

Nugent, A., Kenyon, G., & Porter, R. (2004) · Proceedings. 2004 NASA/DoD Conference on Evolvable Hardware, 2004. · doi:10.1109/eh.2004.1310824 · Cited by 4