Tag: #deep-learning
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LeCun's zip code nets: when convolution met backpropagation (1989)
In 1989 Yann LeCun and colleagues at AT&T Bell Laboratories published a convolutional neural network trained with backpropagation on handwritten US Postal Service zip codes, an early proof that local filters could scale to real mail sorting.
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Neural language models: Bengio's fight against the curse of dimensionality (2003)
In 2003 Yoshua Bengio and colleagues published A Neural Probabilistic Language Model in the Journal of Machine Learning Research, learning distributed word representations to escape the limits of huge n-gram tables.
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Boltzmann machines: when neural networks borrowed statistical physics (1985)
In 1985 David Ackley, Geoffrey Hinton and Terrence Sejnowski published a learning algorithm for Boltzmann machines: stochastic binary units, an energy function from physics, and slow sampling that later methods would try to avoid.
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Deep belief networks: when deep nets became trainable again (2006)
In July 2006 Geoffrey Hinton and colleagues showed that stacking restricted Boltzmann machines layer by layer, then fine-tuning with backpropagation, could train deep networks. The result helped restart the deep-learning revival.
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Backpropagation: how neural networks learned from their mistakes (1986)
In 1986 David Rumelhart, Geoffrey Hinton and Ronald Williams showed that multilayer neural networks trained with backpropagation learn their own internal representations. The method is still the standard way to train most neural networks.
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GANs: when neural networks learned to invent images (2014)
In June 2014 Ian Goodfellow and colleagues introduced generative adversarial networks: two neural networks trained against each other. The idea made realistic image synthesis possible and raised the question of fakes early on.
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AlphaFold wins the 2024 Nobel Prize in Chemistry
One day after AI took the physics prize, the 2024 Chemistry Nobel honored AlphaFold, the DeepMind model that predicts protein structures, and computational protein design.
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The 2024 Nobel Prize in Physics goes to neural networks
John Hopfield and Geoffrey Hinton won the 2024 Nobel Prize in Physics for foundational work on artificial neural networks, the machinery behind today's AI.
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AlexNet: the night deep learning came back (2012)
A deep neural network trained on two gaming <abbr title="Graphics processing unit">GPU</abbr>s crushed the ImageNet benchmark and ended a long winter for neural networks almost overnight.
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AlphaGo vs. Lee Sedol: the machine that learned to play (2016)
DeepMind's AlphaGo beat one of the greatest Go players 4–1, winning not by brute-force search like Deep Blue but by learning intuition from data and self-play.
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ImageNet: the dataset that would teach machines to see (2006)
Fei-Fei Li bet that data, not just cleverer algorithms, would unlock computer vision. ImageNet became the benchmark that lit the fuse for the deep-learning era.