Nov 3, 2016 · The time complexity of our algorithm to find an approximate local minimum is even faster than that of gradient descent to find a critical point.
The time complexity of our algorithm to find an approximate local minimum is even faster than that of gradient descent to find a critical point. Our algorithm ...
Apr 24, 2017 · In this paper we give a provable linear-time algorithm for finding an approximate local minimum in smooth non-convex optimization. It applies to ...
The time complexity of our algorithm to find an approximate local minimum is even faster than that of gradient descent to find a critical point. Our algorithm ...
A non-convex second-order optimization algorithm that is guaranteed to return an approximate local minimum in time which scales linearly in the underlying ...
In this paper we give a provable linear-time algorithm for finding an approximate local minimum in smooth non-convex optimization. It applies to a general ...
Jan 20, 2024 · Gradient descent is taking the first derivative and taking a step towards the minimum by changing all parameters. The key here is that neural ...
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Jan 21, 2019 · So, stochastic gradient descent is more able to avoid local minimum because the landscape of batch loss function is different than the loss ...
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Feb 12, 2019 · Gradient descent DOES NOT find a local minimum. It simply is an iterative method to follow direction of the gradient at a point to perform ...
May 9, 2014 · The best method however of avoiding local minima in neural networks is to use a Gaussian Process model (or a Radial Basis Function neural ...