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Nick Ryder

2 accepted papers

2021

Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer

NeurIPS 2021poster

Hyperparameter (HP) tuning in deep learning is an expensive process, prohibitively so for neural networks (NNs) with billions of parameters. We show that, in the recently discovered Maximal Update Parametrization ($\mu$P), many optimal HPs remain stable even as model size changes. This leads to a ne…

2020

Language Models are Few-Shot Learners

NeurIPS 2020oral

We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any p…