Learning and relearning in Boltzmann machines

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Factor graphs and the sum-product algorithm

Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486 · see citing works

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · see citing works

Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem

McCloskey, M., & Cohen, N. J. (1989) · Psychology of Learning and Motivation · doi:10.1016/s0079-7421(08)60536-8 · Cited by 3,770 · see citing works

Neural Networks and the Bias/Variance Dilemma

Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · see citing works

A practical Bayesian framework for backpropagation networks

MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.448 · Cited by 2,917 · see citing works

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 · see citing works

The highly irregular firing of cortical cells is inconsistent with temporal integration of random epsps.

Softky, W., & Koch, C. (1993) · The Journal of Neuroscience · doi:10.1523/jneurosci.13-01-00334.1993 · Cited by 1,689 · see citing works

The helmholtz machine

Dayan, P., Hinton, G. E., Neal, R. M., & Zemel, R. S. (1995) · Neural Computation · doi:10.1162/neco.1995.7.5.889 · Cited by 1,207 · see citing works

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.03.056 · Cited by 930 · see citing works

Statistically optimal perception and learning: from behavior to neural representations

Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.003 · Cited by 672 · see citing works

The reinterpretation of dreams: An evolutionary hypothesis of the function of dreaming

Revonsuo, A. (2000) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00004015 · Cited by 676 · see citing works

Self-Supervised Video Representation Learning with Odd-One-Out Networks

Fernando, B., Bilen, H., Gavves, E., & Gould, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.607 · Cited by 421 · see citing works

Memory trace reactivation in hippocampal and neocortical neuronal ensembles

Sutherland, G. (2000) · Current Opinion in Neurobiology · doi:10.1016/s0959-4388(00)00079-9 · Cited by 384 · see citing works

On the capabilities of multilayer perceptrons

Baum, E. B. (1988) · Journal of Complexity · doi:10.1016/0885-064x(88)90020-9 · Cited by 379 · see citing works

Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm

O'Reilly, R. C. (1996) · Neural Computation · doi:10.1162/neco.1996.8.5.895 · Cited by 352 · see citing works

Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

Mackay, D. J. C. (1995) · Network Computation in Neural Systems · doi:10.1088/0954-898x/6/3/011 · Cited by 915 · see citing works

Mood, reasoning, and central executive processes.

Oaksford, M., Morris, F., Grainger, B., & Williams, J. M. G. (1996) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/0278-7393.22.2.476 · Cited by 314 · see citing works

Comparative Analysis of Intrusion Detection Systems and Machine Learning Based Model Analysis through Decision Tree

Azam, Z., Islam, M. M., & Huda, M. N. (2023) · IEEE Access · doi:10.1109/access.2023.3296444 · Cited by 265 · see citing works

Bayesian Computation in Recurrent Neural Circuits

Rao, R. P. N. (2004) · Neural Computation · doi:10.1162/08997660460733976 · Cited by 252 · see citing works

A neural network model of retrieval-induced forgetting

Norman, K. A., Newman, E. L., & Detre, G. (2007) · Psychological Review · doi:10.1037/0033-295x.114.4.887 · Cited by 218 · see citing works

Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning

Benedetti, M., Realpe-Gómez, J., Biswas, R., & Perdomo-Ortiz, A. (2016) · Physical Review A · doi:10.1103/physreva.94.022308 · Cited by 215 · see citing works

Facial Expression Recognition: A Survey

Huang, Y., Chen, F., Lv, S., & Wang, X. (2019) · Symmetry · doi:10.3390/sym11101189 · Cited by 195 · see citing works

Video surveillance systems-current status and future trends

Tsakanikas, V., & Dagiuklas, T. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2017.11.011 · Cited by 191 · see citing works

Learning to generate images with perceptual similarity metrics

Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., & Zemel, R. S. (2017) · 2017 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2017.8297089 · Cited by 186 · see citing works

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 · see citing works

Constraints on plasticity in a connectionist model of the English past tense.

Marchman, V. A. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.215 · Cited by 142 · see citing works

Interactive Activation and Mutual Constraint Satisfaction in Perception and Cognition

McClelland, J. L., Mirman, D., Bolger, D. J., & Khaitan, P. (2014) · Cognitive Science · doi:10.1111/cogs.12146 · Cited by 122 · see citing works

Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review

McClelland, J. L. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00503 · Cited by 111 · see citing works

Information Perspective to Probabilistic Modeling: Boltzmann Machines versus Born Machines

Cheng, S., Chen, J., & Wang, L. (2018) · Entropy · doi:10.3390/e20080583 · Cited by 104 · see citing works

The overfitted brain: Dreams evolved to assist generalization

Hoel, E. (2021) · Patterns · doi:10.1016/j.patter.2021.100244 · Cited by 70 · see citing works

Automatic Detection of Standard Sagittal Plane in the First Trimester of Pregnancy Using 3-D Ultrasound Data

Nie, S., Yu, J., Chen, P., Wang, Y., & Zhang, J. Q. (2017) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2016.08.034 · Cited by 42 · see citing works

A new fault diagnosis method based on deep belief network and support vector machine with Teager-Kaiser energy operator for bearings

Han, D., Zhao, N., & Shi, P. (2017) · Advances in Mechanical Engineering · doi:10.1177/1687814017743113 · Cited by 35 · see citing works

Communication and stability in social collectives

Bradley, R. T., & Pribram, K. H. (1998) · Journal of Social and Evolutionary Systems · doi:10.1016/s1061-7361(99)80005-8 · Cited by 32 · see citing works

Deep Learning Based Affective Model for Speech Emotion Recognition

Zhou, X., Guo, J., & Bie, R. (2016) · 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld) · doi:10.1109/uic-atc-scalcom-cbdcom-iop-smartworld.2016.0133 · Cited by 36 · see citing works

Bayesian cognitive science, predictive brains, and the nativism debate

Colombo, M. (2018) · Synthese · doi:10.1007/s11229-017-1427-7 · Cited by 15 · see citing works

Cognitive enhancement mediated through postsynaptic actions of norepinephrine on ongoing cortical activity

Hoshino, O. (2005) · Neural Computation · doi:10.1162/0899766054026675 · Cited by 11 · see citing works

The overfitted brain hypothesis

Prince, L. Y., & Richards, B. A. (2021) · Patterns · doi:10.1016/j.patter.2021.100268 · Cited by 4 · see citing works

Understanding the hippocampus as an apex of the cortical hierarchy and self-supervised predictive learning engine

DeKraker, J., Eichert, N., Hong, S.-J., Evans, A. C., & Bernhardt, B. C. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106890 · Cited by 1 · see citing works