Neural network, Unsupervised learning, Artificial neural network, Artificial intelligence, Machine learning, Convex set

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On Sep 26, 2020
@NandoDF shared
RT @DeepMind: Why does deep learning work so well? Researchers have found a hidden form of regularization in gradient descent - Implicit Gradient Regularization - that biases neural networks towards flat, low test error solutions & may help us answer this question: https://t.co/fyAX362nBK
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Instead, we show that gradient descent follows a path that is closer to the exact continuous path given by θ˙ = −∇θE˜(θ), along a modified loss E˜(θ), which can be calculated analytically using backward error analysis (see Theorem 3.1 and Section 3 for the full analysis), yielding: E˜(θ) ...

arxiv.org
On Sep 26, 2020
@NandoDF shared
RT @DeepMind: Why does deep learning work so well? Researchers have found a hidden form of regularization in gradient descent - Implicit Gradient Regularization - that biases neural networks towards flat, low test error solutions & may help us answer this question: https://t.co/fyAX362nBK
Open

Click here to read the article

Click here to read the article

Instead, we show that gradient descent follows a path that is closer to the exact continuous path given by θ˙ = −∇θE˜(θ), along a modified loss E˜(θ), which can be calculated analytically ...

Accelerating Natural Gradient with Higher-Order Invariance

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Neural network models are trained using stochastic gradient descent and model weights are updated using the backpropagation algorithm. The optimization solved by training a neural network ...

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