gradient-based algorithm is able to do including the constant in a high-dimensional non-convex interference problem and the question would be you know can we apply the same methodology to something that looks more like a supervised neural network simple one we also show that the gradient flow is worse than the larger algorithm that itself is expected but they are both worse than the approximate message passing there is quite a considerable gap so is there some generic kind of a strategy that we that we can make them work as well as