Toward Fast and Accurate Neural Networks for Image Recognition

Posted by Mingxing Tan and Zihang Dai, Research Scientists, Google Research As neural network models and training data size grow, training efficiency is becoming an important focus for deep learning. For example, GPT-3 demonstrates remarkable capability in few-shot learning, but it requires weeks of training with thousands of GPUs, making it difficult to retrain or improve. What if, instead, one could design neural networks that were smaller and faster, yet still more accurate? In this post, we ...

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