Which algorithm updates multimodal models using gradient descent?

The correct answer is the DRaFT+ Algorithm, which is designed specifically to update multimodal models through an efficient gradient descent process. Multimodal models are those that process and analyze data from different modalities, such as text, images, and audio. The DRaFT+ Algorithm leverages gradient descent to minimize loss functions, ensuring that the model adapts and improves its performance across these diverse data types.

Backpropagation, while widely used in training neural networks, serves as the method for computing gradients for weight updates during the training phase rather than being a standalone algorithm specifically tailored for multimodal models. It provides the necessary gradients that facilitate optimization but does not directly relate to the concept of multimodal updates in the same way as the DRaFT+ Algorithm.

The Adam Optimizer is an optimization algorithm that uses gradient descent and is well-known for adaptive learning rates. It can be used to train various types of models, including multimodal models, but it is a more general-purpose optimizer rather than one specifically designed for updating multimodal architectures.

Gradient Boosting is a machine learning technique used primarily for regression and classification tasks that focuses on building models in a sequential manner. It is not typically associated with the training of multimodal models, nor is it