几篇论文实现代码：
《Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots》(CVPR 2022) GitHub: github.com/demonsjin/Blind2Unblind
《Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion》(CVPR 2022) GitHub: github.com/Gutianpei/MID
《NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation》(NeurIPS 2022) GitHub: github.com/JeremieMelo/NeurOLight [fig2]
《Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions》(ACL 2022) GitHub: github.com/xiaolinAndy/RODS [fig7]
《Predicting Out-of-Distribution Error with the Projection Norm》(ICML 2022) GitHub: github.com/yaodongyu/ProjNorm
《ESS: Learning Event-based Semantic Segmentation from Still Images》(ECCV 2022) GitHub: github.com/uzh-rpg/ess [fig5]
《Feature Representation Learning for Unsupervised Cross-domain Image Retrieval》(ECCV 2022) GitHub: github.com/conghuihu/UCDIR [fig6]
《Learning to Learn with Generative Models of Neural Network Checkpoints》(2022) GitHub: github.com/wpeebles/G.pt
《AdaVocoder: Adaptive Vocoder for Custom Voice》(2022) GitHub: github.com/yuan1615/AdaVocoder
《Learning to Compose Soft Prompts for Compositional Zero-Shot Learning》(2022) GitHub: github.com/BatsResearch/csp [fig1]
《MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator》(2022) GitHub: github.com/fgnt/mms_msg
《Motion-Guided-Deep-Dynamic-3D-Garment》(2022) GitHub: github.com/MengZephyr/Motion-Guided-Deep-Dynamic-3D-Garment [fig4]
《From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization》(2022) GitHub: github.com/BUTSpeechFIT/EEND_dataprep
《SPICE: A Dataset for Training Machine Learning Potentials》(2022) GitHub: github.com/openmm/spice-dataset
《Simplified State Space Layers for Sequence Modeling》(2022) GitHub: github.com/lindermanlab/S5
《DPTDR: Deep Prompt Tuning for Dense Passage Retrieval》(2022) GitHub: github.com/tangzhy/DPTDR
《CompoSuite: A Compositional Reinforcement Learning Benchmark》(2022) GitHub: github.com/Lifelong-ML/CompoSuite
《Blind2Unblind: Self-Supervised Image Denoising with Visible Blind Spots》(CVPR 2022) GitHub: github.com/demonsjin/Blind2Unblind
《Stochastic Trajectory Prediction via Motion Indeterminacy Diffusion》(CVPR 2022) GitHub: github.com/Gutianpei/MID
《NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation》(NeurIPS 2022) GitHub: github.com/JeremieMelo/NeurOLight [fig2]
《Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions》(ACL 2022) GitHub: github.com/xiaolinAndy/RODS [fig7]
《Predicting Out-of-Distribution Error with the Projection Norm》(ICML 2022) GitHub: github.com/yaodongyu/ProjNorm
《ESS: Learning Event-based Semantic Segmentation from Still Images》(ECCV 2022) GitHub: github.com/uzh-rpg/ess [fig5]
《Feature Representation Learning for Unsupervised Cross-domain Image Retrieval》(ECCV 2022) GitHub: github.com/conghuihu/UCDIR [fig6]
《Learning to Learn with Generative Models of Neural Network Checkpoints》(2022) GitHub: github.com/wpeebles/G.pt
《AdaVocoder: Adaptive Vocoder for Custom Voice》(2022) GitHub: github.com/yuan1615/AdaVocoder
《Learning to Compose Soft Prompts for Compositional Zero-Shot Learning》(2022) GitHub: github.com/BatsResearch/csp [fig1]
《MMS-MSG: A Multi-purpose Multi-Speaker Mixture Signal Generator》(2022) GitHub: github.com/fgnt/mms_msg
《Motion-Guided-Deep-Dynamic-3D-Garment》(2022) GitHub: github.com/MengZephyr/Motion-Guided-Deep-Dynamic-3D-Garment [fig4]
《From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization》(2022) GitHub: github.com/BUTSpeechFIT/EEND_dataprep
《SPICE: A Dataset for Training Machine Learning Potentials》(2022) GitHub: github.com/openmm/spice-dataset
《Simplified State Space Layers for Sequence Modeling》(2022) GitHub: github.com/lindermanlab/S5
《DPTDR: Deep Prompt Tuning for Dense Passage Retrieval》(2022) GitHub: github.com/tangzhy/DPTDR
《CompoSuite: A Compositional Reinforcement Learning Benchmark》(2022) GitHub: github.com/Lifelong-ML/CompoSuite
【澳洲留学 | 如何高分完成小组作业（Group Work）】
以下3种形式，在我们日常中较为常见：
一、课堂讨论
二、Presentation
三、案例分析
如何才能更好地完成Group Work呢？可以遵循以下几点：
一、合理分工
二、定期了解小组成员的进展情况
三、尊重每个人的意见
四、面对不配合的组员
|essay结构｜report｜dissertation｜proofreading | Methodology|
|留学论文辅导|留学生论文|课程辅导|留学挂科申诉
#留学辅导# #澳洲留学# #澳洲essay#
以下3种形式，在我们日常中较为常见：
一、课堂讨论
二、Presentation
三、案例分析
如何才能更好地完成Group Work呢？可以遵循以下几点：
一、合理分工
二、定期了解小组成员的进展情况
三、尊重每个人的意见
四、面对不配合的组员
|essay结构｜report｜dissertation｜proofreading | Methodology|
|留学论文辅导|留学生论文|课程辅导|留学挂科申诉
#留学辅导# #澳洲留学# #澳洲essay#
IEC 62619 - Scope:
Stationary applications：Electrical energy storage system (ESS)
JIS C 8715-2 – Scope:
据置用途 電力貯蔵装置，これらに類似した用途など。
(Stationary applications: electric power storage, and similar applications.)
UL 1973 – Scope:
Electric energy storage systems for use as energy storage for stationary applications such as for PV
产品应用：通信，UPS，电力储能，叉车，高尔夫球车，AGV，轨道，船舶等
IEC 62619 测试规范
Stationary applications：Electrical energy storage system (ESS)
JIS C 8715-2 – Scope:
据置用途 電力貯蔵装置，これらに類似した用途など。
(Stationary applications: electric power storage, and similar applications.)
UL 1973 – Scope:
Electric energy storage systems for use as energy storage for stationary applications such as for PV
产品应用：通信，UPS，电力储能，叉车，高尔夫球车，AGV，轨道，船舶等
IEC 62619 测试规范
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