Bi-RNN

Overview of Entity Relation Extraction and Related Conference Papers

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Overview of Entity Relation Extraction and Related Conference Papers

Every day we bring you NLP technology insights! Introduction Entity Relation Extraction is a core task of text mining and information extraction. It mainly models text information to automatically extract semantic relationships between entity pairs, thereby extracting valid semantic knowledge. The research results are mainly applied in text summarization, automatic question answering, machine translation, semantic … Read more

Comprehensive Overview of Three Feature Extractors in NLP (CNN/RNN/TF)

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Comprehensive Overview of Three Feature Extractors in NLP (CNN/RNN/TF)

Source: AI Technology Review This article contains over 10,000 words, and it is recommended to read it in about 20 minutes. In this article, author Zhang Junlin uses vivid language to compare the features of the three major feature extractors in natural language processing (CNN/RNN/TF). At the turn of the year, everyone is busy reviewing … Read more

Understanding Attention Mechanisms in Deep Learning

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Understanding Attention Mechanisms in Deep Learning

The attention mechanism is potentially a very useful method. In this issue, let’s understand the principles and methods behind the attention mechanism. The original text is in English from https://blog.heuritech.com/2016/01/20/attention-mechanism/ With the development of deep learning and artificial intelligence, many researchers are interested in the “attention mechanism” in neural networks. This article aims to provide … Read more

Understanding Attention Mechanism in Neural Networks

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Understanding Attention Mechanism in Neural Networks

Click I Love Computer Vision to get CVML new technologies faster This article is an interpretation of the commonly used Attention mechanism in papers by 52CV fans, reprinted with the author’s permission. Please do not reprint: https://juejin.im/post/5e57d69b6fb9a07c8a5a1aa2 Paper Title: “Attention Is All You Need” Authors: Ashish Vaswani Google Brain Published in: NIPS 2017 Introduction Remember … Read more

Hardcore Introduction to NLP – Seq2Seq and Attention Mechanism

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Hardcore Introduction to NLP - Seq2Seq and Attention Mechanism

Click the top “MLNLP” to select the “Starred” public account. Heavyweight content delivered first-hand. From:Number Theory Legacy The prerequisite knowledge for this article includes:Recurrent Neural NetworksRNN, Word EmbeddingsWordEmbedding, Gated UnitsVanillaRNN/GRU/LSTM. 1 Seq2Seq Seq2Seq is the abbreviation for sequence to sequence. The first sequence is called the encoder encoder, which is used to receive the source … Read more

Understanding Attention Mechanism in Language Translation

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Understanding Attention Mechanism in Language Translation

Author丨Tianyu Su Zhihu Column丨Machines Don’t Learn Address丨https://zhuanlan.zhihu.com/p/27769286 In the previous column, we implemented a basic version of the Seq2Seq model. This model performs sorting of letters, taking an input sequence of letters and returning the sorted sequence. Through the implementation in the last article, we have gained an understanding of the Seq2Seq model, which mainly … Read more

Distilling Llama3 into Hybrid Linear RNN with Mamba

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Distilling Llama3 into Hybrid Linear RNN with Mamba

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

Distilling Llama3 into Hybrid Linear RNN with Mamba

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Distilling Llama3 into Hybrid Linear RNN with Mamba

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, covering NLP master’s and doctoral students, university teachers, and researchers from enterprises. The Community’s Vision is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning at home and abroad, especially … Read more

Comparative Study of Transformer and RNN in Speech Applications

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Comparative Study of Transformer and RNN in Speech Applications

Original link: https://arxiv.org/pdf/1909.06317.pdf Abstract Sequence-to-sequence models are widely used in end-to-end speech processing, such as Automatic Speech Recognition (ASR), Speech Translation (ST), and Text-to-Speech (TTS). This paper focuses on a novel sequence-to-sequence model called the Transformer, which has achieved state-of-the-art performance in neural machine translation and other natural language processing applications. We conducted an in-depth … Read more

Who Will Replace Transformer?

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Who Will Replace Transformer?

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering graduate students, faculty, and researchers in NLP. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. Reprinted from | AI Technology Review … Read more