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Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity Jingzhao Zhang, Tianxing He, Suvrit Sra, Ali Jadbabaie Published: 19 Dec 2019, Last Modified: 05. ABSTRACT We provide a theoretical explanation for the effectiveness of gradient clipping in training deep neural networks. The key ingredient is a new smoothness condition derived. Abstract: The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly. However, our. We introduce EPISODE, an algorithm for federated learning with heterogeneous data under the relaxed smoothness setting for training deep neural networks, and provide state-of-the-art.