Input Representation

Input representation, the method of encoding data for machine learning models, significantly impacts model performance and generalization. Current research focuses on optimizing input representations for various modalities (text, images, audio, time series) using techniques like specialized model fusion, information-theoretic approaches, and novel architectures such as transformers and convolutional neural networks. These advancements aim to improve model accuracy, robustness, and efficiency across diverse applications, including natural language processing, computer vision, and signal processing, by addressing issues like overfitting, hallucination, and order dependency.