Paper (17) 썸네일형 리스트형 [GNN] Node2Vec: Scalable Feature Learning for Networks 0. Abstract Previsous Study predction task -> feature를 학습시키는 과정에서 많은 발전 BUT connectivity pattern을 제대로 학습 못함 This Paper low dimensional feature learning & maximizing likelihoods of nodes flexible notion of nodes ("neighborhood node" 너무 엄격한 구분 X) 1. Node2Vec semi supervisded algorithm supervised : expensive for real-word unsupervised : generalize X SGD motivation #Flexibility #Generalization u, s6.. [GNN] Deepwalk : online learning of social representations https://arxiv.org/pdf/1403.6652.pdf 0. Main Idea Graph data --> low-dim dense representation "Embedding" Graph --> NLP method --> Embedding Graph --> Random pattern --> create NL sequence (random walk sequence) random walk sequence --> Skip-Gram algorithm --> Node Embedding 1. Random walk Deep Walk : 그래프에서 sequence를 생성해, 자연어처리의 Skip-Gram 방식으로 임베딩을 학습 이 때, “그래프에서 sequence를 생성”하는 과정 = Random Walk .. [NLP] ERNIE: Enhanced Representation through Knowledge Integration https://arxiv.org/abs/1904.09223 [NLP] GPT-2 : Language Models are Unsupervised Multitask Learners https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf 0. Abstract zero-shot setting BUT good performance & underfits WebText learn to perform tasks from their naturally occurring demonstrations -> promising! 1. Introduction current ML -> sensitive to data dist' -> narrow experts GPT-2 = more general systems which can perform many tasks #Multitask l.. [OCR] ViT-STR: Vision Transformer for Fast and Efficient SceneText Recognition https://arxiv.org/pdf/2105.08582.pdf https://github.com/roatienza/deep-text-recognition-benchmark GitHub - roatienza/deep-text-recognition-benchmark: PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficien PyTorch code of my ICDAR 2021 paper Vision Transformer for Fast and Efficient Scene Text Recognition (ViTSTR) - GitHub - roatienza/deep-text-recognition-benchmark: PyTorch.. [OCR] FOTS: Fast Oriented Text Spotting with a Unified Network https://arxiv.org/pdf/1801.01671.pdf https://github.com/jiangxiluning/FOTS.PyTorch GitHub - jiangxiluning/FOTS.PyTorch: FOTS Pytorch Implementation FOTS Pytorch Implementation. Contribute to jiangxiluning/FOTS.PyTorch development by creating an account on GitHub. github.com 0. Abstract FOTS : detection & recognition -->simultaneous & complementary ( computational&visual information 공유) 1. Introd.. [OCR] Donut : Document Understanding Transformer without OCR https://arxiv.org/pdf/2111.15664v1.pdf 0. Abstract OCR framework에서 벗어난 E2E model synthetic document image generator -> large scale data에 대한 의존 낮춤 1. Introduction Semi-structured documents 기존 VDU (Visual Document Understanding) 보통 OCR 기반 분리된 세 개의 모듈로 구성 : text detection, text recognition, parsing 문제점 1: OCR is expensive and is not always available 문제점 2: OCR errors negatively influence subsequent.. [OCR] TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models https://arxiv.org/pdf/2109.10282.pdf https://github.com/microsoft/unilm/tree/master/trocr GitHub - microsoft/unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities - GitHub - microsoft/unilm: Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities github.com .. 이전 1 2 3 다음
