Multi-Head RAG: Multi-Head Attention Activation Layer for Document Retrieval

Multi-Head RAG: Multi-Head Attention Activation Layer for Document Retrieval

Source: DeepHub IMBA This article is about 2500 words long and suggests a reading time of 9 minutes. This paper proposes a new scheme that utilizes the multi-head attention layer of the decoder model instead of the traditional feed-forward layer activation. The existing RAG solutions may suffer because the embeddings of the most relevant documents … Read more

Professor E Wei Nan’s New Work: Memory3 in Large Models

Professor E Wei Nan's New Work: Memory3 in Large Models

Reported by Machine Heart Editor: Chen Chen A 2.4B Memory3 outperforms larger LLM and RAG models. According to a message from the WeChat public account of Machine Heart: In recent years, large language models (LLMs) have gained unprecedented attention due to their extraordinary performance. However, the training and inference costs of LLMs are high, and … Read more

15 Typical RAG Frameworks in 2024

15 Typical RAG Frameworks in 2024

A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future DirectionsThis article delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. RAG effectively enhances output accuracy by combining retrieval and generation models, overcoming the limitations of LLMs. The study details the architecture of RAG, demonstrating how retrieval … Read more

Transformers in Computer Vision

Transformers in Computer Vision

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

Transformers and Their Variants in NLP

Transformers and Their Variants in NLP

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

Layer-by-Layer Function Introduction and Detailed Explanation of Transformer Architecture

Layer-by-Layer Function Introduction and Detailed Explanation of Transformer Architecture

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

Position-Temporal Awareness Transformer for Remote Sensing Change Detection

Position-Temporal Awareness Transformer for Remote Sensing Change Detection

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

Comparative Study of Transformer and RNN in Speech Applications

Comparative Study of Transformer and RNN in Speech Applications

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

Understanding Transformer and Its Variants

Understanding Transformer and Its Variants

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

Overlooked Details of BERT and Transformers

Overlooked Details of BERT and Transformers

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