Jan 29, 2024 · Our main theoretical contribution is to prove that the training error of small networks converges linearly to a (non-null) constant, of which we ...
Dec 19, 2023 · Our main theoretical contribution is to prove that the training error of small networks converges linearly to a (non-null) constant, of which we ...
Jan 9, 2024 · Our main theoretical contribution is to prove that the training error of small networks converges linearly to a (non-null) constant, of which we ...
In this work, we focus on the case of small (or underparameterized) networks. An advantage of small networks is that they are faster to train while retaining ...
Our main theoretical contribution is to prove that the training error of small networks converges linearly to a (non-null) constant, of which we give a precise ...
We hope this will be fixed in the near future. Published. Engineering Sciences. Training and Generalization Errors for Underparameterized Neural Networks.
We perform an analysis of the average generalization dynamics of large neural networks trained using gradient descent.
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Apr 24, 2024 · If the training error is low but the generalization error is high, it means that the model is overfitting the training data. Is the reverse ...
Missing: Underparameterized | Show results with:Underparameterized
Jun 15, 2020 · This paper analyzes the relation between smoothness and low generalization error in an overparameterized linear learning problem.
In this paper, we examine the optimal generalization error in strategies involving DeNNs versus WeNNs. Our finding suggests that when the parameter count in ...