Author: An Sheng & Yan Yongqiang, Datawhale Members This article has approximately10,000 words, divided into modules to interpret and practice the Transformer. It is recommended tosave and read. In 2017, Google proposed a model called Transformer in a paper titled “Attention Is All You Need,” which is based on the attention (self-attention mechanism) structure to … Read more
Transformers, as an attention-based encoder-decoder architecture, have not only revolutionized the field of Natural Language Processing (NLP) but have also made groundbreaking contributions to the field of Computer Vision (CV). Compared to Convolutional Neural Networks (CNNs), Vision Transformers (ViT) rely on excellent modeling capabilities, achieving outstanding performance on several benchmarks including ImageNet, COCO, and ADE20k. … Read more
The 2023 Zhejiang Programmer Festival is in full swing. As part of the series of events for the Programmer Festival, the Knowledge Push activity will successively launch knowledge sharing on the topic of [Artificial Intelligence], including the development of AI large models, cutting-edge technologies, learning resources, etc. Stay tuned! This issue’s content is: Unleashing the … Read more
Source: DeepHub IMBA This article is 4000 words long, and it is recommended to read it in 10 minutes. This study not only points out a long-ignored technical issue but also provides important optimization directions for future model training practices. When fine-tuning large-scale language models (LLMs) in a local environment, it is often difficult to … Read more
Source: CSDN Blog Author: Jay Alammar This article is about 7293 words, suggested reading time 14 minutes。 This article introduces knowledge related to the Transformer, using a simplified model to explain core concepts one by one. The Transformer was proposed in the paper “Attention is All You Need” and is now recommended as a reference … Read more
The Transformer has become a mainstream model in the field of artificial intelligence, widely applied across various domains.However, the attention mechanism in Transformers is computationally expensive, and this cost continues to rise with increasing sequence length. To address this issue, many innovative modifications of Transformers have emerged in the industry to optimize their operational efficiency. … Read more
Today, let’s talk about Transformers. To make it easy for everyone to understand, we will explain it in simple language. If you need, feel free to click the “Click to Copy” below to receive it for free! Transformer Transformers can be described as a type of super brain designed to process sequential data, such as … Read more
Source | Heart of Autonomous Driving Editor | Deep Blue Academy Abstract Transformers, an encoder-decoder model based on attention, have revolutionized the field of Natural Language Processing (NLP). Inspired by these significant achievements, recent pioneering work has adopted transformer-like architectures in the field of Computer Vision (CV), demonstrating their effectiveness in three fundamental CV tasks … Read more
Do language models plan for future tokens? This paper provides the answer. “Don’t let Yann LeCun see this.” Yann LeCun said it’s too late; he has already seen it. Today, we will introduce a paper that “LeCun must see,” exploring the question: Is the Transformer a far-sighted language model? When it performs inference at a … Read more
Source: New Intelligence [ Introduction ] Recently, researchers from Tsinghua University and Ant Group re-examined the application of the Transformer structure in time series analysis, proposing a completely new inverted perspective—achieving comprehensive leadership in time series prediction tasks without modifying any modules! In recent years, Transformers have made continuous breakthroughs in natural language processing and … Read more