This article is reprinted from AI Park. Author: Cheng He Translation: ronghuaiyang Introduction Applying Transformers to CV tasks is becoming increasingly common, and here we organize some related advancements for everyone. The Transformer architecture has achieved state-of-the-art results in many natural language processing tasks. One major breakthrough of the Transformer model may be the release … Read more
Follow the WeChat public account “ML_NLP“ Set it as “Starred“, delivering heavy content directly to you! Author: Jiang Runyu, Harbin Institute of Technology SCIR Introduction In recent years, the most impressive achievement in the field of NLP is undoubtedly the pre-trained models represented by Google’s BERT. They continuously break records (both in task metrics and … Read more
Source: Deephub Imba This article has a total of 2700 words, recommended reading time is 5 minutes. This article will give you an understanding of the overall architecture of the Transformer. For many years, deep learning has been continuously evolving. Deep learning practice emphasizes the use of a large number of parameters to extract useful … Read more
Click the above “Beginner’s Guide to Vision“, select to add “Star” or “Top“ Important content delivered at the first time A Position-Temporal Awareness Transformer for Remote Sensing Change Detection Position-Temporal Awareness Transformer for Remote Sensing Change Detection Authors: Yikun Liu, Kuikui Wang, Mingsong Li, Yuwen Huang, Gongping Yang Abstract With the development of deep learning, … Read more
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
Follow the public account "ML_NLP" Set as “Starred“, heavy content will be delivered to you first! Author: Jiang Runyu, Harbin Institute of Technology SCIR Introduction In recent years, one of the most impressive achievements in the field of NLP is undoubtedly the pre-trained models represented by BERT proposed by Google. They continuously refresh records (both … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and corporate researchers. 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 | … Read more
Follow our public account to discover the beauty of CV technology 1 Introduction Due to its superior capability for modeling global dependencies, the Transformer and its variants have become the primary architecture for many visual and language tasks. However, tasks like Visual Question Answering (VQA) and Referencing Expression Comprehension (REC) often require multi-modal predictions that … Read more
Source: Huang Yu Zhihu This article is about 3600 words long, and it is recommended to read it in 10 minutes. This article introduces the review paper "Efficient Transformers: A Survey" published by Google in September last year, which states that in the field of NLP, transformers have successfully replaced RNNs (LSTM/GRU), and applications have … Read more