Post
1601
🇺🇸 🇨🇦 🇬🇧 Nobel Prize winners against USSR & Japanese AI pioneers ☭🇯🇵
🇩🇪 Prof. Jürgen Schmidhuber: “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
1965 - First Deep Learning - USSR ☭ (Ukraine 🇺🇦 now)
Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
1969 - Rectified linear unit - Japan 🇯🇵
In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
1970 - Backpropagation - Finland 🇫🇮 😃
In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
1972 - Recurrent Neural Network - Japan 🇯🇵
In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
1979 - First Convolutional neural network - Japan 🇯🇵
CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
🇩🇪 Prof. Jürgen Schmidhuber: “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
1965 - First Deep Learning - USSR ☭ (Ukraine 🇺🇦 now)
Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
1969 - Rectified linear unit - Japan 🇯🇵
In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
1970 - Backpropagation - Finland 🇫🇮 😃
In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
1972 - Recurrent Neural Network - Japan 🇯🇵
In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
1979 - First Convolutional neural network - Japan 🇯🇵
CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
