Jun 29, 2023 · The sampling is based on the idea of random feature models. However, instead of a data-agnostic distribution, e.g., a normal distribution, we ...

We introduce sampled networks, which are neural networks where each pair of weight and bias of all hidden layers is completely determined by two points from the ...

Nov 12, 2023 · In numerical experiments, we demonstrate that sampled networks achieve accuracy comparable to iteratively trained ones, but can be constructed.

Jun 29, 2023 · The sampling is based on the idea of random feature models. However, instead of a data-agnostic distribution, e.g., a normal distribution, we ...

May 30, 2024 · The sampling is based on the idea of random feature models. However, instead of a data-agnostic distribution, e.g., a normal distribution, we ...

This work proposes a new strategy to train DL models by Learning Optimal samples Weights (LOW), making better use of the available data. LOW determines how much ...

Dec 9, 2023 · In numerical experiments, we demonstrate that sampled networks achieve accuracy comparable to iteratively trained ones, but can be constructed ...

Jul 28, 2023 · The sampling is based on the idea of random feature models. However, instead of a data-agnostic distribution, e.g., a normal distribution, we ...

sample a weight that captures the distribution of classes in the training set: less represented classes should receive higher weights in the loss function to ...

Dec 10, 2023 · Scientific paper: Sampling weights of deep neural networks Abstract: We introduce a probability distribution, combined with an efficient…