Uniform a priori bounds and error analysis for the Adam stochastic gradient descent optimization method

This paper provides the first complete mathematical proof that the Adam optimizer (the most popular method for training AI neural networks) actually works reliably for a broad class of optimization problems. The key breakthrough is proving that Adam stays bounded during training, which allows researchers to guarantee it will converge to good solutions without making risky assumptions.