isolation transformer

Analysis Report on DeepSeek-V2 MLA (Multi-Head Latent Attention) Technology

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Analysis Report on DeepSeek-V2 MLA (Multi-Head Latent Attention) Technology

1. Core Objective MLA (Multi-Head Latent Attention) is an innovative attention mechanism designed to address the inference efficiency issues caused by the excessively large KV cache in traditional Transformer models. Its core objective is to significantly reduce memory usage (KV cache) during inference through low-rank compression and decoupled positional encoding, while maintaining or even improving … Read more

Understanding Core Principles of DeepSeek-MLA

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Understanding Core Principles of DeepSeek-MLA

DeepSeek’s MLA (Multi-head Latent Attention) is an improved attention mechanism technique based on the Transformer architecture (combining multi-head attention and latent variables), aimed at enhancing the model’s understanding and processing capabilities of input data. By introducing latent variables and sparse attention, MLA can better capture the hidden structure of input data while reducing computational overhead, … Read more

New Architecture for GNN Hand-Drawn Sketch Recognition: Multi-Graph Transformer Network

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New Architecture for GNN Hand-Drawn Sketch Recognition: Multi-Graph Transformer Network

Click I Love Computer Vision to star and get faster access to new CVML technologies This article introduces a relatively niche but very interesting new paper on hand-drawn sketch recognition “Multi-Graph Transformer for Free-Hand Sketch Recognition”, which essentially proposes a novel Transformer network. Author information: All authors are from Nanyang Technological University. I. Research Motivation … Read more

Efficient Neural Network Architecture for Mobile Applications

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Efficient Neural Network Architecture for Mobile Applications

↑ ClickBlue TextFollow the Jishi platformAuthor丨Pai Pai XingSource丨CVHub Jishi Introduction This article presents a simple yet efficient modern inverted residual mobile module designed for mobile applications. The proposed efficient model (Efficient MOdel, EMO) achieves excellent overall performance on the ImageNet-1K, COCO2017, and ADE20K benchmarks, surpassing the SOTA models based on CNN/Transformer at the same computational … Read more

Multi-Token Prediction (MTP) in V3: Inference Acceleration

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Multi-Token Prediction (MTP) in V3: Inference Acceleration

Last time we discussed the structure of the Deepseek model. Today, I will share the inference acceleration tool in V3, the multi-token prediction (MTP) module, which is somewhat similar to speculative decoding, but the specific code has not been open-sourced. Speculative decoding was introduced in a series of articles on inference optimization, which essentially changes … Read more

Introduction to Core Technologies in Neural Networks

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Introduction to Core Technologies in Neural Networks

In recent years, artificial intelligence technology has been changing our lives at an astonishing pace. From intelligent voice assistants to autonomous driving, from medical image analysis to personalized recommendations, AI technology has permeated all aspects. Are you curious about what supports these magical functions? Today, we will unveil the technical core of the AI world … 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

Detailed Steps for Fine-Tuning Large Models with LoRA

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Detailed Steps for Fine-Tuning Large Models with LoRA

📚 Fine-Tuning Series Articles Understanding the Development and Evolution of Fine-Tuning Technology Estimated reading time: 5 minutes With the widespread application of large-scale Transformer models (such as GPT, LLaMA, ViT), the computational and storage costs of fine-tuning large models have become limiting factors. LoRA, as a Parameter-Efficient Fine-Tuning (PEFT) technique, effectively reduces resource consumption by … 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