Today, I would like to introduce a powerful deep learning model: CNN-LSTM-Attention! This model combines three different types of neural network architectures, fully exploiting the spatial and temporal information in the data. It not only captures the local features and long-term dependencies of the data but also automatically focuses on the most important parts of … Read more
Follow the official account “ML_NLP“ Set as “Starred“, essential resources delivered first-hand! Looking back at the phrase from 2017, “Attention is all you need”, it truly was a prophetic statement. The Transformer model started with machine translation in natural language processing, gradually influencing the field (I was still using LSTM in my graduation thesis in … Read more
This article is approximately 3900 words long and is recommended for an 8-minute read. In this article, we demonstrate that Causal Attention can be rewritten in the form of an RNN. In recent years, RNNs have rekindled interest among researchers and users due to their linear training and inference efficiency, hinting at a “Renaissance” in … Read more
Click the "Xiaobai Learns Vision" above, select to add "star" or "top" Heavyweight content delivered to you first Author丨Baifeng@Zhihu (Authorized) Source丨https://zhuanlan.zhihu.com/p/632301499 Editor丨Jishi Platform Jishi Guide Surpassing fine-tuning, LoRA, VPT, etc. with only a small number of parameters fine-tuned! Paper link: https://arxiv.org/pdf/2305.15542 GitHub link: https://github.com/bfshi/TOAST We found that when fine-tuning large models on a downstream task, … Read more
Research on CNN-BiLSTM Short-term Power Load Forecasting Model Based on Attention Mechanism and ResNet WANG Lize1,2, XIE Dong1,2*, ZHOU Lifeng1,2, WANG Hanqing1,2 (1.School of Civil Engineering, University of South China, Hengyang, Hunan 421001, China;2.Hunan Engineering Laboratory of Building Environmental Control Technology, University of South China, Hengyang, Hunan 421001, China) Abstract：Short term power load forecasting is … Read more
Follow us on WeChat: ML_NLP. Set as a “Starred” account for heavy content delivered to you first! Source: | Zhihu Link: | https://zhuanlan.zhihu.com/p/40920384 Author: | Yuanche.Sh Editor: | Machine Learning Algorithms and Natural Language Processing WeChat account This article is for academic sharing only. If there is any infringement, please contact us to delete it. … Read more
XU Wanxu, SHEN Yindong (School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, Hubei 430074) Abstract: Traditional bus travel time prediction models often ignore information from historical timestamps, leading to unsatisfactory prediction accuracy. To address the temporal nature of bus travel times, this paper proposes a prediction model based on the … Read more
Click I Love Computer Vision to get CVML new technologies faster This article is an interpretation of the commonly used Attention mechanism in papers by 52CV fans, reprinted with the author’s permission. Please do not reprint: https://juejin.im/post/5e57d69b6fb9a07c8a5a1aa2 Paper Title: “Attention Is All You Need” Authors: Ashish Vaswani Google Brain Published in: NIPS 2017 Introduction Remember … Read more
Follow the public account “ML_NLP“ Set as “Starred” for heavy content delivered first! “ This article provides a deep analysis of the Transformer model, including the overall architecture, the background and details of the Attention structure, the meanings of QKV, the essence of Multi-head Attention, FFN, Positional Embedding, and Layer Normalization, as well as everything … Read more
Machine Learning Algorithms and Natural Language Processing(ML-NLP) is one of the largest natural language processing communities both domestically and internationally, gathering over 500,000 subscribers, covering NLP master’s and doctoral students, university teachers, and corporate researchers. Community Vision is to promote communication and progress between the academic and industrial circles of natural language processing and enthusiasts … Read more