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Nvidia AI Introduces the Normalized Transformer nGPT
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A new NVIDIA AI research paper describes the development of a new neural network architecture called the Normalized Transformer (nGPT). This architecture utilizes representation learning on the hypersphere, which involves normalizing all embedding vectors and network matrices to reside on a unit norm hypersphere. This normalization eliminates the need for weight decay and promotes faster convergence during training, achieving the same accuracy in fewer steps than traditional Transformers. The text also outlines the modifications made to the baseline Transformer to create nGPT, including the use of eigen learning rates to control the contribution of attention and MLP blocks. The paper provides experimental results demonstrating the significant acceleration of training with nGPT, analyzes the learned parameters of the network, and conducts ablation studies to examine the impact of various design choices. Finally, the text discusses the theoretical underpinnings of nGPT, drawing connections to Riemannian optimization and other related works in the field of representation learning. • Read the full article here: https://www.marktechpost.com/2024/10/... • Paper: https://arxiv.org/abs/2410.01131 • Audio Created by NotebookLLM and reviewed by real human. • #machinelearning #artificialintelligence #deeplearning #datascience #ai @NVIDIADeveloper @NVIDIA @LatestNvidiaNews • Don’t Forget to join our 55k+ ML SubReddit: / machinelearningnews
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