Universality Results in Gradient Descent Neural Network Training

Researchers have proven a universality result for neural networks, demonstrating that any network can be redesigned to be trainable via gradient descent if a solution exists. This theoretical framework offers significant insights into the fundamental capabilities and limitations of meta-learning architectures.