Geoffrey E. Hinton

Geoffrey E. Hinton’s indexed work has fundamentally reshaped machine learning, beginning with his first-author 2006 *Science* paper on reducing dimensionality with neural networks and a companion *Neural Computation* paper introducing a fast learning algorithm for deep belief nets, which together revived interest in deep architectures. As senior author, he led the 2015 *Nature* review "Deep learning" (over 82,000 citations) and the landmark 2017 *Communications of the ACM* paper on ImageNet classification with deep convolutional neural networks (over 75,000 citations), which demonstrated the power of large-scale supervised learning. His 2008 *Journal of Machine Learning Research* paper on t-SNE for visualizing high-dimensional data (over 35,000 citations) remains a standard tool for data exploration. Beyond these lead-author contributions, Hinton’s 2014 paper "Dropout: a simple way to prevent neural networks from overfitting" (over 34,000 citations) introduced a widely adopted regularization technique. His other first-author works include distilling knowledge in neural networks (2015, over 14,000 citations), rectified linear units for restricted Boltzmann machines