Feb 20, 2024 · In this paper, we develop novel generalization error bounds for a class of unrolled DNNs that are informed by a compound Gaussian prior.
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Novel generalization error bounds are developed for a class of unrolled DNNs that are informed by a compound Gaussian prior and have been shown to ...
The generalization error bound is formulated by bounding the Rademacher complexity of the class of compound Gaussian network estimates with Dudley's integral.
Oct 11, 2024 · In this paper, we apply a chain rule for Gaussian complexity (Maurer, 2016a) to analyze how low-rank layers in deep networks can prevent the ...
Missing: Compound | Show results with:Compound
Feb 21, 2024 · Algorithm unfolding or unrolling is the technique of constructing a deep neural network (DNN) from an iterative algorithm.
Jan 12, 2022 · We study here how the local geometry of the energy landscape around local minima affects the statistical properties of SGD with Gaussian gradient noise.
Missing: Compound | Show results with:Compound
Algorithm unfolding or unrolling is the technique of constructing a deep neural network (DNN) from an iterative algorithm. Unrolled DNNs often provide ...
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This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning.
If stagnation occurs, we derive a bound on the generalization error of deep neural networks involving the spectral norms of the weight matrices but not the.
On Generalization Bounds for Deep Compound Gaussian Neural Networks. Resource URI: https://dblp.l3s.de/d2r/resource/publications/journals/corr/abs-2402 ...