Dec 25, 2020 · We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss criterion's convexification.
Dec 25, 2020 · We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss criterion's ...
Jun 7, 2023 · Momentum: Use momentum-based optimization techniques (e.g., SGD with momentum, Adam) which can help the model escape local minima by ...
Jun 20, 2023 · One commonly used approach is to use advanced optimization algorithms that can help the model escape local minima and find better global minima.
Adaptively Solving the Local-Minimum Problem for Deep Neural Networks ... This paper aims to overcome a fundamental problem in the theory and application of deep ...
Dec 25, 2020 · This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve ...
Oct 4, 2018 · Stochastic gradient descent (often abbreviated SGD) is an iterative method for optimizing an objective function with suitable smoothness properties.
Missing: Adaptively | Show results with:Adaptively
Feb 4, 2022 · At local minima, the function value is smaller than the nearby points. While training neural networks, we try to minimise the loss function.
Missing: Adaptively | Show results with:Adaptively
This paper continues the discussion of weight evolution algorithm for solving the local minimum problem of back-propagation by changing the weights of a ...
Missing: Adaptively Deep
May 14, 2024 · Adapts learning rate based on recent gradients. Can still get stuck in local minima on non-convex problems. Non-convex optimization problems.