Introduction The Next item recommendation system is one of the core components of modern online services, embedded in applications such as music, video, and e-commerce websites, helping users navigate and discover new content. Generally, the system is modeled as a sequence prediction task, often implemented over recurrent neural networks or other generative sequence models. Its … Read more
MLNLP ( Machine Learning Algorithms and Natural Language Processing ) community is a well-known natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university professors, and corporate researchers. The vision of the community is to promote communication between the academic and industrial circles of natural language processing and machine learning, … Read more
This article delves into three key attention mechanisms in Transformer models: self-attention, cross-attention, and causal self-attention. These mechanisms are core components of large language models (LLMs) like GPT-4 and Llama. By understanding these attention mechanisms, we can better grasp how these models work and their potential applications. We will discuss not only the theoretical concepts … Read more
Click the "Xiaobai Studies Vision" above, select "Star" or "Top" to receive essential information promptly. Is CNN a Type of Local Self-Attention? Author: Hohou https://www.zhihu.com/question/448924025/answer/1791134786 (This answer references: Li Hongyi’s 2021 Machine Learning Course) CNN is not a type of local attention, so let’s analyze what CNN and attention are doing. 1: CNN can be … Read more
Delivering NLP technical insights to you every day! © Author | Zeng Weihao Institution | Beijing University of Posts and Telecommunications Research Direction | Dialogue Summarization Typesetting | PaperWeekly Paper Title: Adversarial Self-Attention For Language Understanding Paper Source: ICLR 2022 Paper Link: https://arxiv.org/pdf/2206.12608.pdf Introduction This paper proposes the Adversarial Self-Attention mechanism (ASA), which reconstructs the … 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
Click on the "Xiaobai Learns Vision" above, select "Star" or "Pin" Heavy content delivered first This article is compiled from Zhihu Q&A, used for academic sharing only, copyright belongs to the author. If there is any infringement, please contact the backend for deletion. Viewpoint One Author | Guohao Li https://www.zhihu.com/question/366088445/answer/1023290162 Let’s talk about my understanding. … Read more
MLNLP(Machine Learning Algorithms and Natural Language Processing) community is one of the largest natural language processing communities in China and abroad, gathering over 500,000 subscribers, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic and industrial communities of natural … Read more
Originally from New Machine Vision Source: towardsdatascience Author: Raimi Karim Edited by: Xiao Qin [Introduction]The recent rapid advancements in the field of NLP are closely related to architectures based on Transformers. This article guides readers to fully understand the self-attention mechanism and its underlying mathematical principles through diagrams and code, and extends to Transformers. BERT, … Read more
Self-Attention Mechanism The Self-Attention mechanism (Self-Attention)https://so.csdn.net/so/search?q=Self-Attention&spm=1001.2101.3001.7020, as a type of attention mechanism, is also known as intra Attention. It is an important component of the famous Transformer model. It allows the model to allocate weights within the same sequence, thereby focusing on different parts of the sequence to extract features. This mechanism is very effective … Read more