구 @zero_grad
딥러닝 깔짝이는 사람 Joined May 2021-
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@CVPR Actually, curious why this didn't trigger the arXiv plagiarism detector. It is possible that not all are on arXiv, but see e.g. the following very similar passages (from unflagged arxiv.org/abs/2206.07578 and arxiv.org/abs/2106.15580). CC @tdietterich, if you have any thoughts.
근데 표절 검사 프로그램은 한국에서만 쓰나? 저정도면 무조건 걸릴텐데.. 석논쓸 때 표절률 몇퍼 이런 서류 제출하라고 했눈디
I suspect that one incentive that drives people to plagiarism is when the language of publication (English) is different from their primary language (likely Korean in this case). In addition to better peer review, perhaps one way to reduce plagiarism is to reduce that incentive.
I suspect that one incentive that drives people to plagiarism is when the language of publication (English) is different from their primary language (likely Korean in this case). In addition to better peer review, perhaps one way to reduce plagiarism is to reduce that incentive.
Here we share a video to present how our E2V-SDE paper (that has been accepted to #CVPR2022) largely consists of texts that are uncredited verbatim copies from more than 10 previously published papers. Enjoy! youtube.com/watch?v=UCmkpL… #CVPR22 @CVPR @CSProfKGD @CVPRConf
Codeball - AI가 수행하는 코드 리뷰 news.hada.io/topic?id=6654 - 딥러닝을 이용해서 PR을 자동으로 체크하고 문제없으면 승인하는 로봇 - 변경부분 diff, Repo 및 Coder의 최근 Activity, Coder의 Reputation 등 100여개의 indicator를 이용 - 백만개의 PR로 훈련됨 - Multi-layer Pe...
Yesterday, I discovered the amazing StudioGAN Torch library! 😻 It's super clean and easy to use and it has most of the popular GAN models (StyleGAN3, BigGAN, etc.) github.com/POSTECH-CVLab/…
A free Deep Learning course using Pytorch and starting at the very beginning. Includes some cool stuff: • Transformers • Generative models • Self-supervised learning Check the attached overview. deeplearning.neuromatch.io/tutorials/intr…
Large Language Models are Zero-Shot Reasoners Simply adding “Let’s think step by step” before each answer increases the accuracy on MultiArith from 17.7% to 78.7% and GSM8K from 10.4% to 40.7% with GPT-3. arxiv.org/abs/2205.11916
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