LSTM + Attention Mechanism is very useful for improving the prediction accuracy of models when processing long sequence data, making it a powerful tool! For example, the MALS-Net model combines these two elements, achieving a significant accuracy improvement of 47.7% in predictions. The main advantage lies in LSTM’s ability to learn long-term dependencies, making it … Read more
Follow the WeChat public account “ML_NLP“ and set it as a “starred“, for heavy content delivered first-hand! Reprinted from | GiantPandaCV [GiantPandaCV Introduction] In recent years, Attention-based methods have gained popularity in academia and industry due to their interpretability and effectiveness. However, the network structures proposed in papers are often embedded in code frameworks for … Read more
This article will coverthe essence of Attention,the principle of Attention and its applications to help you understand the Attention mechanism. 1.The Essence of Attention The core logic: from focusing on everything to focusing on key points The Attention mechanism can grasp the key points when processing long texts, without losing important information. The Attention mechanism … Read more
Click belowCard, follow the “CVer” public account AI/CV heavy content, delivered first time Click to enter—>【Object Detection and Transformer】 group chat Reprinted from: Machine Heart | Edited by: Egg Sauce, Xiao Zhou SVM is all you need, support vector machines never go out of style. Transformer is a support vector machine (SVM), a new theoretical … Read more
This article shares a detailed blog post about the Transformer from Harvard University, translated by our lab. The Transformer architecture proposed in the paper “Attention is All You Need” has recently attracted a lot of attention. The Transformer not only significantly improves translation quality but also provides a new structure for many NLP tasks. Although … Read more
By/Gao Kaiyuan Image Source: Internet Introduction [email protected] I have been reviewing materials related to Paddle, so I decided to take a closer look at the source code of Baidu’s ERNIE. When I skimmed through it before, I noticed that ERNIE 2.0 and ERNIE-tiny are quite similar to BERT. I wonder what changes have been made … Read more
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