This paper confirms that as the number of layers increases, this bias becomes more closely associated with an imbalance in the distribution of eigenvector centrality, known as localization, which further amplifies the discrepancy in label influence on nodes, resulting in a performance gap.
May 13, 2024
May 17, 2024 · ABSTRACT. One of the byproducts of message passing neural networks (MPNNs) is their potential bias towards weakly connected nodes, which can.
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Duration: 2:02
Posted: Mar 26, 2024
Posted: Mar 26, 2024
May 15, 2024 · We have examined whether the superficial layers of the superior colliculus (SC) provide the source of visual signals that guide the ...
Jul 10, 2016 · The main reason is that more layers imply more non-linearities. E.g. for convnets ReLU units are added on top of each convolutional layer, so ...
Dec 3, 2022 · Larger/deeper neural networks can model higher-dimensional functions and, therefore, more complex problems. That's just math and not up for ...
Aug 20, 2023 · The "inherent bias" comes from the fact that a less deep neural network will have to take on broader tasks, and thus make more assumptions about ...
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Feb 23, 2017 · Adding more layers to a neural network can improve accuracy by allowing the network to learn and represent more complex and abstract features ...
Mar 19, 2010 · A layer in a neural network without a bias is nothing more than the multiplication of an input vector with a matrix. (The output vector might be ...
Feb 1, 2023 · Because the deeper layers are modeling high-frequency components of the target function, they need a larger model capacity to fit well than the ...