Question: I have searched various materials and read the original papers, which detail how Q, K, and V are obtained through certain operations to derive output results. However, I have not found any explanation of where Q, K, and V come from. Isn’t the input to a layer just a tensor? Why do we have … Read more
Follow the public account “ML_NLP“ Set as “Starred“, heavy content delivered immediately! From | Zhihu Author | lllltdaf Link | https://www.zhihu.com/question/325839123 Editor | Public Account of Machine Learning Algorithms and Natural Language Processing This article is for academic sharing only. If there is infringement, please contact the background for deletion. As someone who does CV, … Read more
This article assumes you are familiar with the Transformer Attention mechanism. If not, that’s okay; let me explain briefly. The Attention mechanism refers to the focus point; the same event can have different focal points for different people. For instance, the teacher says: “Xiao Ming skipped class again to play basketball.” The teacher’s focus is … Read more
↑ ClickBlue Text Follow the Jishi Platform Author丨mayiwei1998 Source丨GiantPandaCV Reprinted from丨Jishi Platform Abstract Due to the network structures in many papers being typically embedded within code frameworks, the code tends to be quite redundant. The author of this article has organized and reproduced the core code based on Attention networks from recent years. Author Information: … Read more
Machine Heart Column Machine Heart Editorial Team 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 on sequence length in large language models significantly restricts their applications in artificial intelligence, such as multi-turn dialogue, long … Read more
Selected from MachineLearningMastery Author: Jason Brownlee Translated by Machine Heart Contributors: Nurhachu Null, Lu Xue The encoder-decoder structure has shown advanced levels in several fields, but this structure encodes the input sequence into a fixed-length internal representation. This limits the length of the input sequence and results in poorer performance of the model on particularly … Read more
1 New Intelligence Compilation Source: distill.pub/2016/augmented-rnns Authors: Chris Olah & Shan Carter, Google Brain Translator: Wen Fei Today is September 10, 2016 Countdown to AI WORLD 2016 World Artificial Intelligence Conference: 38 days Countdown for Early Bird Tickets: 9 days [New Intelligence Guide] The Google Brain team, led by Chris Olah & Shan Carter, has … Read more
“The stone from other hills can serve to polish jade.” Only by standing on the shoulders of giants can we see further and go farther. On the path of scientific research, we need to leverage favorable conditions to move forward faster. Therefore, we have specially collected and organized some practical code links, datasets, software, programming … Read more
Introduction In the fields of Natural Language Processing (NLP), Computer Vision (CV), and other deep learning domains, the Attention mechanism has become a crucial tool. It helps models focus on the most critical parts while processing large amounts of information, significantly improving performance. For many Python learners new to deep learning, understanding and mastering the … Read more
Machine Heart reports Machine Heart Editorial Team “Big model developers, you are wrong.” “I discovered a bug in the attention formula that no one has found for eight years. All Transformer models, including GPT and LLaMA, are affected.” Yesterday, a statistician named Evan Miller stirred up a storm in the AI field with his statement. … Read more