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Rethinking Neural Network Efficiency: Beyond Parameter Counting to Practical Data Fitting

Marktechpost

JUNE 22, 2024

Neural networks, despite their theoretical capability to fit training sets with as many samples as they have parameters, often fall short in practice due to limitations in training procedures. Key technical aspects include the use of various neural network architectures (MLPs, CNNs, ViTs) and optimizers (SGD, Adam, AdamW, Shampoo).

Rethinking Neural Network Efficiency: Beyond Parameter Counting to Practical Data Fitting

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