Jun 20, 2023 · To overcome the problem of getting stuck in local minima in machine/deep learning optimization, there are several techniques you can employ. One commonly used approach is …

Oct 24, 2017 · The local-minimum problem in training deep learning machines (DLMs) has plagued their development. This paper proposes a method to directly solve the problem. Our …

Apr 23, 2020 · If your test set is stuck at the same accuracy, the reason is probably that the data you are using for your training/validation dataset does not generalize well enough on your test …

Oct 6, 2020 · How will the neural network get unstuck from that local minimum. We use momentum-based optimization techniques to avoid local minimums. Some other variants of …

Jun 1, 2023 · Change the learning rate: A smaller learning rate may help the model to escape from the local minimum. Use a different optimization algorithm: A different optimization …

Nov 4, 2024 · Local minima issues especially affect deep and complex networks, which can suffer from long training times and convergence to suboptimal performance. Escaping these areas …

Sep 20, 2023 · While training we see a growing difference in the minimum and maximum of our predicted labels with a much lower loss value too (see loss output below). However, despite …

Nov 14, 2017 · This paper proposes a method to directly solve the problem. Our method is based on convexification of the sum squared error (SSE) criterion through transforming the SSE into …

Feb 17, 2021 · I have tried every pytorch optimizer with various learning rates and hyperparameters but they all get stuck in the local minimum at {W,b}=0. I have also tried a …

Jan 12, 2022 · If we use a noisy gradient, a gradient that points in different directions, rather than in one direction, our problem is solved. This is called stochastic gradient descent (SGD) or …