Description: Neural interpolation is a method that allows estimating intermediate values between known data points using neural networks. This approach is based on the ability of neural networks to learn complex patterns in data, enabling them to generalize and make predictions about unobserved values. In the context of neural networks, interpolation becomes a powerful tool, as these networks are particularly well-suited for working with various types of data, including temporal sequences. They can remember information from previous inputs due to their architecture, allowing them to capture long-term dependencies and perform more accurate interpolations. Neural interpolation is not limited to value estimation; it can also be used to smooth noisy data, fill gaps in incomplete datasets, and enhance prediction quality across various applications. Its relevance lies in its ability to handle nonlinear and complex data, making it a valuable technique in fields such as time series forecasting, signal processing, and generative modeling, among others.
