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
Selected from arXiv Authors: Andrea Banino et al. Compiled by Machine Heart Editors: Chen Ping, Du Wei Researchers from DeepMind proposed the CoBERL agent for reinforcement learning, which combines a new contrastive loss with a hybrid LSTM-transformer architecture to improve data processing efficiency. Experiments show that CoBERL can continuously improve performance across the entire Atari … Read more
↑ ClickBlue Text Follow the Jishi PlatformAuthor丨ChaucerGSource丨Jizhi ShutongEditor丨Jishi Platform Jishi Introduction This article introduces Sequencer, a brand new and competitive architecture that can replace ViT, providing a fresh perspective for classification problems. Experiments show that Sequencer2D-L achieves 84.6% top-1 accuracy on ImageNet-1K with only 54M parameters. Moreover, the authors demonstrated its good transferability and robustness … Read more
Research on Land Subsidence Intelligent Prediction Method Based on LSTM and Transformer: A Case Study of Shanghai PENG Wenxiang1,2,3,4,5，ZHANG Deying1,2,3,4,5 1. Shanghai Institute of Geological Survey, Shanghai 200072, China; 2. Shanghai Institute of Geological Exploration Technology, Shanghai 200072, China; 3. Key Laboratory of Land Subsidence Monitoring and Prevention, Ministry of Natural Resources of China, Shanghai … Read more
Selected from arXiv Author: Zhonghui You et al. Translated by Machine Heart Contributors: Siyuan, Yiming Using the basic idea of LSTM’s gating mechanism for pruning? Let the model decide which convolution kernels can be discarded. Remember when we understood LSTM, we found that it uses a gating mechanism to remember important information and forget unimportant … Read more
Selected from Machine Learning Mastery Author: Jason Brownlee Translated by Machine Heart Contributed by: Li Zenan How should we cope when LSTM recurrent neural networks face long sequence inputs? Jason Brownlee provides us with 6 solutions. Long Short-Term Memory (LSTM) recurrent neural networks can learn and remember long sequences of input. If your problem has … Read more
Machine Heart Release Authors: Song Zhuoran, Wang Ru, Ru Dongyu, Peng Zhenghao, Jiang Li Shanghai Jiao Tong University In this article, the authors utilize the Dropout method to generate a large amount of sparsity during the neural network training process for acceleration. This paper has been accepted by the Design Automation and Test in Europe … Read more
Hello everyone, I am Hua Ge. This article systematically and comprehensively organizes the introduction and algorithm principles of various deep learning models. At the beginning of the article, let me first introduce our company’s popular business. If you have any needs or ideas, feel free to chat! 1 Main Text Deep learning methods utilize neural … Read more
Click the "Learn Visuals" above, and select to add "Star" or "Pin to Top". Important resources delivered to you first. Author on Zhihu | masterSu Link | https://zhuanlan.zhihu.com/p/139617364 This article is about 3200 words, recommended reading time 5 minutes This article introduces the visualization of the LSTM model structure. Recently, I have been learning about … Read more
1. Introduction The neural network algorithm is an important branch of artificial intelligence. It constructs models that can learn and adapt by simulating the connection patterns of neurons in the human brain. In many application scenarios, neural network algorithms have demonstrated powerful performance and potential. However, building an excellent neural network model is not an … Read more