Google & Northwestern U Present Provably Efficient Learning Algorithms for Neural Networks | Synced

A research team from Google Research and Northwestern University presents polynomial time and sample-efficient algorithms for learning an unknown depth-2 feedforward neural network with general ReLU activations, aiming to provide insights into whether efficient algorithms exist for learning ReLU networks.