Jan 18, 2018 · Stochastic gradient descent (SGD) with small batchsizes appears to locate minima with better generalization properties than large-batch SGD.
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Abstract: We consider a number of popular beliefs within the neural network community on the training and generalization behavior of multilayer perceptrons, ...
Jan 18, 2018 · There is a quantity which predicts generalization, penalizes sharp minima, and doesn't depend on the parameterization. Its the evidence/marginal ...
Dive into the research topics of 'Local minima and generalization'. Together they form a unique fingerprint. Global Minimum Keyphrases 100%. Number of Data ...
Abstract. Recent advances in deep learning theory have evoked the study of generalizability across differ- ent local minima of deep neural networks (DNNs).
ABSTRACT. We consider a number of popular beliefs within the neural network community on the training and generalization behavior of multi-layer perceptrons ...
Nov 4, 2018 · So even the spurious local minima is not an issue, keeping generalization as our main goal. Another issue which could hamper the learning rate ...
Sep 8, 2018 · Overfitting and getting stuck in a local minimum are not the same thing. You could get stuck in a local minimum yet generalize well.
Oct 21, 2023 · Recent advances in deep learning theory have evoked the study of generalizability across different local minima of deep neural networks (DNNs) ...
But local minima aren't a problem for modern neural networks that are ...
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Dropout's main motivation is not to break local minima. It's to achieve better generalization. If it were the case that it was meant to break bad minima, we ...