1. Multi-Head Attention Multi-Head Attention is a widely adopted extension of the attention mechanism in the Transformer model. It captures different attention distributions in various subspaces of the input sequence by running multiple independent attention mechanisms in parallel, thereby comprehensively capturing the various semantic associations present in the sequence. In Multi-Head Attention, the input sequence … Read more
Full text link:https://tecdat.cn/?p=38195 The stock market plays a significant role in economic development. Due to the high return characteristics of stocks, the stock market has attracted increasing attention from institutions and investors. However, due to the complex volatility of the stock market, it can sometimes lead to significant losses for institutions or investors. Considering the … Read more
Title:A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classification Paper Link: https://www.sciencedirect.com/science/article/abs/pii/S1051200425000922 Two-Branch Feature Extraction Module (TBFE): Utilizes 2D and 3D convolutions in parallel to extract spatial and spectral features, effectively fusing multidimensional information. Hybrid Pooling Attention Module (HPA): Combines average pooling and max pooling to achieve information aggregation across spatial dimensions, … Read more
Title:A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classification Paper Link: https://www.sciencedirect.com/science/article/abs/pii/S1051200425000922 Proposed synergistic CNN-Transformer network, combining the local feature extraction capability of CNNs with the global modeling advantages of Transformers, while processing the spatial and spectral information of HSI. Designed Two-Branch Feature Extraction (TBFE) module, which utilizes 3D convolution (focusing on … Read more
Title:A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classification Paper Link:https://doi.org/10.1016/j.dsp.2025.105070 Collaborative CNN-Transformer Architecture Design A synergistic CNN-Transformer network is proposed, combining the local spatial feature extraction capability of CNNs with the global modeling capability of Transformers, effectively achieving joint modeling of spectral and spatial information in hyperspectral images (HSI). Two-Branch Feature … Read more
Title:A synergistic CNN-transformer network with pooling attention fusion for hyperspectral image classification Paper link: https://doi.org/10.1016/j.dsp.2025.105070 1. Proposed Synergistic CNN-Transformer Network Structure: Combines the local spatial feature extraction capabilities of CNNs with the global spectral modeling capabilities of Transformers to comprehensively extract spatial-spectral features from hyperspectral images (HSI).2. Twin-Branch Feature Extraction Module (TBFE): Parallel combination of … Read more
Zhihu: Jiang Fuchun (Authorized) Link: https://zhuanlan.zhihu.com/p/16730036197 Editor: “Deep Learning Natural Language Processing” WeChat Official Account Introduction DeepSeek has recently gained significant attention, and I have been following some of the technical reports released by DeepSeek. They have consistently surprised everyone with their model training, inference performance, and computational costs. After reading DeepSeek’s technical reports, I … Read more
🔸 Paper Information Title:Joint Optimization Strategies for Multi-Service Communication and Computing Resources in 6G MEC Authors:：Wang Desheng, Deng Ke, Huang Zhihua, Zhang Hao, Lin Haohan First Author Affiliation:School of Electronic Information and Communications, Huazhong University of Science and Technology Abstract:The system latency and energy consumption of edge computing technology (MEC) are taken as the core … Read more
Introduction The previous article “Implementing LLM from Scratch with 200 Lines of Python” created a “poetry generator” starting from a “probabilistic” implementation, ultimately using PyTorch to realize a classic Bigram model. In the Bigram model, each character is only related to the previous character. Despite this, our <span>babygpt_v1.py</span> also outputs sentences like “Gradually realizing the … Read more
Content reference from “2022 Best Practices for Edge Computing Implementation”, Source: SDN/NFV/AI Standards and Industry Promotion Committee.As MEC technology spans multiple fields including OT, IT, and CT, it involves network connectivity, data aggregation, as well as chips, sensors, and industry applications. To better meet the business needs of different industries, more open collaboration, joint … Read more