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목록논문리뷰 (19)
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[NLP] Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System, PPTOD 리뷰
제목 : Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System 저자 : Yixuan Su, Lei Shu, Elman Mansimov, Arshit Gupta, Deng Cai, Yi-An Lai, Yi Zhang 링크 : https://arxiv.org/abs/2109.14739 Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, e..
[NLP] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer 리뷰(T5)-3
저자: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu 링크 : https://arxiv.org/abs/1910.10683 Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful techniq..
[NLP] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer 리뷰(T5)-2
저자: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu 링크 : https://arxiv.org/abs/1910.10683 Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful techniq..
[NLP] Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer 리뷰(T5)-1
저자: Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu 링크 : https://arxiv.org/abs/1910.10683 Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful techniq..
[CNN] ImageNet Classification with Deep ConvolutionalNeural Networks
제목 : ImageNet Classification with Deep ConvolutionalNeural Networks 저자 : Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton 링크 : https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf 이번주에는 Alex Net 으로도 알려져 있는 ImageNet Classification with Deep ConvolutionalNeural Networks 논문을 읽어 보았습니다. 무려 2021년 11월 기준 90000회가 넘는 인용수를 가진 엄청난 논문이었습니다. 논문을 읽으면서 느낀점은, 논문을 읽는다는 느낌이..
[Summarazation] A Neural Attention Model for Abstractive Sentence Summarization
논문 링크 : https://arxiv.org/abs/1509.00685 A Neural Attention Model for Abstractive Sentence Summarization Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a loca arxiv.org 2015 년에 나온 논문으로 인용수가 무려 20..
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
논문 링크 : https://arxiv.org/abs/1502.03167 이번주에 공부한 내용은 Batch Normalization입니다. 처음에는 논문만 읽고 내용을 이해해 보려고 했는데 지식이 부족해서 논문만 읽고 전체 내용을 이해하기에 한계가 있어서 블로그 글을 몇개 더 읽어보고 내용을 정리 해 보았습니다. 일단 이 논문을 읽어보고 싶었던 이유는! bert에 들어가는 구조이었기 때문입니다.! 리뷰! 신경망 학습은 loss function을 미분한 뒤, 이 기울기를 parameter에 반영하는 방식으로 이루어집니다. 이 기울기가 너무 크거나 작은 경우 학습이 제대로 이뤄지지 않습니다. 그래서 이 문제를 해결하기 위해 ReLU와 같은 활성화 함수를 쓰기도 하고, 가중치 초기값을 잘 두는 방법, smal..
[NLP 논문리뷰]STraTA: Self-Training with Task Augmentation for Better Few-shot Learning
제목 : HowMuchKnowledge Can You Pack Into the Parameters of a Language Model? 저자 : Tu Vu, Minh-Thang Luong, Quoc V. Le, Grady Simon, Mohit Iyyer, Noam Shazeer 발행년도 : 2021 paper : https://arxiv.org/abs/2109.06270 code : https://github.com/google-research/google-research/tree/master/TA-ST Review Abstract 최근의 발전이 NLP task에서 많은 발전을 이뤘지만, Large scale의 pretrained 언어 모델이 few shot 세팅에서는 그리 좋은 성능을 보이지 못하고 ..
[NLP논문리뷰]How Much Knowledge Can You Pack Into the Parameters of a Language Model?
제목 : HowMuchKnowledge Can You Pack Into the Parameters of a Language Model? 저자 : Adam Roberts, Colin Raffel, Noam Shazeer 발행년도 : 2020 paper : https://arxiv.org/abs/2002.08910 code : https://github.com/google-research/google-research/tree/master/t5_closed_book_qa Review QA모델에 대한 논문 중 google에서 출간한 논문. 기존의 QA task는 passage를 보여주고 이에 대한 답을 찾아가는 방식이었다. 그러나 이 논문은 passage를 보여주지 않고 질문 부터 한다. 즉 다양한 task에 ..