Follow the WeChat public account “ML_NLP” Set as “Starred” to receive heavy content promptly! Reprinted from | PaperWeekly ©PaperWeekly Original · Author｜Hai Chenwei School｜Master’s student at Tongji University Research Direction｜Natural Language Processing In the current NLP field, Transformer/BERT has become a fundamental application, and Self-Attention is the core part of both. Below, we attempt to … Read more
Source: Python Data Science This article is about 7200 words, recommended reading time 14 minutes. In this article, we will explore the Transformer model and understand how it works. 1. Introduction The BERT model launched by Google achieved state-of-the-art results in 11 NLP tasks, triggering a revolution in the NLP field. One key factor for … Read more
来源：DeepHub IMBA This article is approximately 1800 words long and is recommended to be read in 5 minutes. This article will provide a detailed introduction to the principles and mechanisms of the masks in attention mechanisms. The attention mechanism mask allows us to send batches of data of varying lengths into the transformer at once. … Read more
Reprinted from: Machine Heart Lightning Attention-2 is a new type of linear attention mechanism that aligns the training and inference costs of long sequences with those of a 1K sequence length. The limitation of sequence length in large language models greatly restricts their applications in the field of artificial intelligence, such as multi-turn dialogue, long … Read more
Source: Heart of Autonomous Driving Editor: Deep Blue Academy Since the emergence of VIT, Transformers have sparked a revolution in the CV field, leading to significant advancements in various upstream and downstream tasks. Today, we will review the end-to-end object detection algorithms based on Transformers! Original Transformer Detector DETR (ECCV2020) The pioneering work! DETR! Code … Read more
Lightning Attention-2 is a new type of linear attention mechanism that aligns the training and inference costs of long sequences with those of a 1K sequence length. The limitations on sequence length in large language models greatly restrict their applications in the field of artificial intelligence, such as multi-turn dialogue, long text understanding, and the … Read more
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
Click the above“Beginner Learning Vision”, choose to add “Star Mark” or “Pin” Important content delivered at the first time Editor’s Recommendation This article summarizes the main content of Teacher Li Hongyi’s introduction to various attention mechanisms in the Spring 2022 Machine Learning course, which also serves as a supplement to the 2021 course. Reprinted from丨PaperWeekly … Read more
Author: Sienna Reviewed by: Los Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities in numerous language processing tasks; however, the computational intensity and memory consumption required for their deployment have become significant challenges to improving service efficiency. Industry estimates suggest that the processing cost of a single LLM request can be as much as … Read more
Doctoral Innovation Forum Issue Seventy On the morning of March 1, 2024, the seventieth issue of the Doctoral Innovation Forum was held online.PhD student Qin Yubin from Tsinghua University’s School of Integrated Circuits presented an academic report titled “High-Efficiency Attention Model Architecture Design”. The report focuses on the attention-based Transformer model, discussing optimization methods for … Read more