Attention Mechanism

Overview of Non-Targeted Uptake and Toxicity Mechanisms of ADC Drugs

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Overview of Non-Targeted Uptake and Toxicity Mechanisms of ADC Drugs

Source: YaoDu Author: Cheng Chuan Yuan Hang Editor: Wan Zi 1 Introduction Antibody-Drug Conjugates (ADCs) are a novel class of highly effective biopharmaceuticals that link antibodies (Antibody) to biologically active small-molecule cytotoxic payloads (Payload) via linkers (Linker). ADCs aim to enhance the therapeutic index (TI) of chemotherapeutic agents by more selectively delivering cytotoxic drugs to … Read more

China Telecom’s 5G Black Technology: Multi-RAT, Modular MM, and PON Transmission

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China Telecom's 5G Black Technology: Multi-RAT, Modular MM, and PON Transmission

This week, the 2017 IMT-2020 (5G) Summit, hosted by the IMT-2020 (5G) Promotion Group, opened in Beijing. Yang Fengyi, Deputy Director of the China Telecom Technology Innovation Center, stated in his speech that China Telecom is actively participating in standardization and related work, both in 3GPP and IMT-2020. China Telecom has submitted over 160 proposals … Read more

Mastering RNNsearch, Multi-task, and Attention Models in Machine Translation

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Mastering RNNsearch, Multi-task, and Attention Models in Machine Translation

Machine Heart Column This column is produced by Machine Heart SOTA! Model Resource Station and is updated weekly on the Machine Heart WeChat public account every Sunday. This column will review common tasks in fields such as natural language processing and computer vision, and provide detailed explanations of classic models that have achieved SOTA on … Read more

Understanding Multi-Head Attention in NLP

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Understanding Multi-Head Attention in NLP

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

Predicting Bank Stock Prices in China Using a CNN-LSTM-ARIMA Hybrid Model with Attention Mechanism

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Predicting Bank Stock Prices in China Using a CNN-LSTM-ARIMA Hybrid Model with Attention Mechanism

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

A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification

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A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification

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

A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification

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A Synergistic CNN-Transformer Network with Pooling Attention Fusion for Hyperspectral Image Classification

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

DSP 2025: Plug-and-Play Fusion Pooling Attention Mechanism, Continuously Open Source

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DSP 2025: Plug-and-Play Fusion Pooling Attention Mechanism, Continuously Open Source

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

(DSP 2025) Hyperspectral Image Classification Module: Plug-and-Play and Completely Crazy

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(DSP 2025) Hyperspectral Image Classification Module: Plug-and-Play and Completely Crazy

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

Technical Interpretation of DeepSeek (1) – A Comprehensive Understanding of MLA (Multi-Head Latent Attention)

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Technical Interpretation of DeepSeek (1) - A Comprehensive Understanding of MLA (Multi-Head Latent Attention)

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