The Slingshot Mechanism: An Empirical Study of Adaptive Optimizers and the Grokking Phenomenon
This paper was accepted to the “Has it Trained Yet?” (HITY) workshop at NeurIPS 2022. The grokking phenomenon as reported by Power et al., refers to a regime where a long period of overfitting is followed by a seemingly sudden transition to perfect generalization. In this paper, we attempt to reveal the underpinnings of Grokking via a series of empirical studies. Specifically, we uncover an optimization anomaly plaguing adaptive optimizers at extremely late stages of training, referred to as the Slingshot Mechanism. A prominent artifact of the Slingshot Mechanism can be measured by the cyclic…
Adaptive gradient methods, notably Adam, have become indispensable for optimizing neural networks, particularly in conjunction with Transformers. In this paper, we present a novel optimization anomaly called the Slingshot Effect, which manifests during extremely late stages of training. We identify a distinctive characteristic of this phenomenon through cyclic phase transitions…
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they are particularly susceptible to gradient heterogeneity across layers, unlike…
Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper, we perform an empirical evaluation on methods…