👉 Tensor-Based Multimodal Learning (TBML) is a cutting-edge approach in machine learning that integrates and leverages multiple types of data, such as text, images, and audio, by utilizing tensor representations. Tensors are multi-dimensional arrays that generalize vectors and matrices, allowing TBML to effectively capture complex relationships across different modalities. This method employs neural networks designed to process and combine these tensor inputs, enabling models to learn rich, cross-modal representations. By doing so, TBML can perform tasks like multimodal classification, translation, and generation with improved accuracy and efficiency, making it particularly useful in applications like virtual assistants, content creation, and personalized recommendations.
