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).
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