Abstract
As artificial intelligence systems evolve to handle complex multimodal data, traditional neural network optimization methods face limitations in simultaneously managing heterogeneous input-output relationships. This paper proposes a novel optimization framework for neural networks that computes cumulative loss across multiple distinct input-output pairs. Each pair may involve unique data modalities and may use different optimization algorithms — such as Adam, SGD, or NAG — enabling more flexible and context-sensitive training.
Unlike conventional loss functions that aggregate a uniform loss across the network, our method permits tailored optimization strategies for each pair, which are then integrated into a cumulative loss function. This cumulative loss influences updates to all neurons in the network, including those not directly linked to a given input-output pair, due to full propagation through intermediate neurons.