This articleis approximately 5200 words, recommended reading time is10+minutes We know that, to some extent, the deeper the network, the more parameters it has, and the more complex the model, the better its final performance. The compression algorithm for neural networks aims to transform a large and complex pre-trained model into a streamlined smaller model. … Read more
Follow our public account to discover the beauty of CV technology This article is reprinted from Machine Heart. The key to the tremendous success of the Transformer in deep learning is the attention mechanism. The attention mechanism allows Transformer-based models to focus on parts of the input sequence that are relevant, achieving better contextual understanding. … Read more
New Intelligence Report Author: Gaode Intelligent Technology Center [New Intelligence Guide]The research and development team of Gaode Intelligent Technology Center designed a contrastive learning framework for knowledge distillation in their work, and proposed COS-NCE LOSS based on this framework. This paper has been accepted by AAAI 2021. NLP (Natural Language Processing) plays an important role … Read more
Big Data Digest authorized reprint from Data Pie Compiled by:Sun Siqi, Cheng Yu, Gan Zhe, Liu Jingjing In the past year, there have been many breakthrough advancements in the research of language models, such as GPT, which generates sentences that are convincingly realistic [1]; BERT, XLNet, RoBERTa [2,3,4], etc., have swept various NLP rankings as … Read more
Large models based on the Transformer architecture are playing an increasingly important role in artificial intelligence, especially in the fields of natural language processing (NLP) and computer vision (CV). Model compression methods reduce their memory and computational costs, which is a necessary step for implementing Transformer models on practical devices. Given the unique architecture of … Read more