Geoffrey Hinton
Nobel Laureate in Physics 2024 | Godfather of Deep Learning | Professor Emeritus, Univ. of Toronto
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Geoffrey Hinton. Every entry undergoes editorial source verification.
ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
Co-authored the milestone AlexNet paper that crushed the ImageNet competition in 2012 by an unprecedented 10.8% margin, demonstrating the power of GPU-accelerated convolutional networks with ReLU activations and Dropout regularization.
Required heavy data augmentation (cropping, flipping, PCA jittering) to prevent severe overfitting on large parameter weights.
Learning Representations by Back-Propagating Errors
Authored the foundational 1986 Nature paper demonstrating that backward propagation of error gradients enables multi-layer neural networks to learn internal representations, resolving the perceptron limits and founding modern connectionist AI.
Susceptible to vanishing/exploding gradients in extremely deep architectures prior to residual connections and normalized activations.