👉 Gain computing is a computational approach that aims to enhance the performance of neural networks by focusing on the most informative parts of the input data, rather than processing all features equally. It works by computing a gain for each input feature based on its relevance to the task at hand, typically using a weighted sum of the features. This gain is then used to determine which features contribute more significantly to the network's output, effectively reducing noise and improving the model's ability to generalize. By concentrating on the most informative features, gain computing can lead to more efficient and accurate models, particularly in scenarios with high-dimensional or noisy data.
