20220204 #金钟云[超话]##艺声[超话]##Yesung[超话]# 更新一则【转载请注明金钟云吧】 &&:El día que reuní a las personas que querían ir a una finca vinícola y lo compartí ¿Cuándo podré volver a ir..?
:那我把想去葡萄酒庄园的人召集起来分享,我什么时候才能去……?(原文中文)
:那我把想去葡萄酒庄园的人召集起来分享,我什么时候才能去……?(原文中文)
几篇论文实现代码:
《One Chatbot Per Person: Creating Personalized Chatbots based on Implicit Profiles》(SIGIR 2021) GitHub:https:// github.com/zhengyima/DHAP
《Learning to Track with Object Permanence》(ICCV 2021) GitHub:https:// github.com/TRI-ML/permatrack [fig3]
《JoJoGAN: One Shot Face Stylization》(2021) GitHub:https:// github.com/mchong6/JoJoGAN [fig1]
《A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code》(2021) GitHub:https:// github.com/ropas/pytea
《Towards a Unified View of Parameter-Efficient Transfer Learning》(2021) GitHub:https:// github.com/jxhe/unify-parameter-efficient-tuning [fig2]
《PantheonRL: A MARL Library for Dynamic Training Interactions》(2021) GitHub:https:// github.com/Stanford-ILIAD/PantheonRL
《Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark》(NeurIPS 2021) GitHub:https:// github.com/iamalexkorotin/Wasserstein2Benchmark [fig4]
《Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data》(2021) GitHub:https:// github.com/sberbank-ai/Real-ESRGAN
《FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark》(2021) GitHub:https:// github.com/mlii0117/FFA-IR
《The neural architecture of language: Integrative modeling converges on predictive processing》(2021) GitHub:https:// github.com/mschrimpf/neural-nlp
《One Chatbot Per Person: Creating Personalized Chatbots based on Implicit Profiles》(SIGIR 2021) GitHub:https:// github.com/zhengyima/DHAP
《Learning to Track with Object Permanence》(ICCV 2021) GitHub:https:// github.com/TRI-ML/permatrack [fig3]
《JoJoGAN: One Shot Face Stylization》(2021) GitHub:https:// github.com/mchong6/JoJoGAN [fig1]
《A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code》(2021) GitHub:https:// github.com/ropas/pytea
《Towards a Unified View of Parameter-Efficient Transfer Learning》(2021) GitHub:https:// github.com/jxhe/unify-parameter-efficient-tuning [fig2]
《PantheonRL: A MARL Library for Dynamic Training Interactions》(2021) GitHub:https:// github.com/Stanford-ILIAD/PantheonRL
《Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark》(NeurIPS 2021) GitHub:https:// github.com/iamalexkorotin/Wasserstein2Benchmark [fig4]
《Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data》(2021) GitHub:https:// github.com/sberbank-ai/Real-ESRGAN
《FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark》(2021) GitHub:https:// github.com/mlii0117/FFA-IR
《The neural architecture of language: Integrative modeling converges on predictive processing》(2021) GitHub:https:// github.com/mschrimpf/neural-nlp
Now that the weather is a bit better, Dazzler has decided to meet up with a fellow elf as well as Irish wolfhounds Méabh and Saoirse to help show him some more sights that Clare has to offer. #FindDazzler
Mar gheall go bhfuil an aimsir beagáinín níos fearr anois, rinne Dazzler an cinneadh casadh le síogaí agus leis na cúnna faoil Méabh agus Saoirse chun cabhrú leis roinnt radhairc eile i gContae an Chláir a fheiceáil. #CáBhfuilDazzler
Mar gheall go bhfuil an aimsir beagáinín níos fearr anois, rinne Dazzler an cinneadh casadh le síogaí agus leis na cúnna faoil Méabh agus Saoirse chun cabhrú leis roinnt radhairc eile i gContae an Chláir a fheiceáil. #CáBhfuilDazzler
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