Domain

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

A pytorch implementation of CyCADA. If you use this code in your research please consider citing @inproceedings{Hoffman_cycada2017,       authors = {Judy Hoffman and Eric Tzeng and Taesung Park and Jun-Yan Zhu,             and Phillip Isola and Kate Saenko and Alexei A. Efros and Trevor Darrell},       title = {CyCADA: Cycle Consistent Adversarial Domain Adaptation},       booktitle = {International Conference on Machine Learning (ICML)},       year = 2018} Setup Check out the repo (recursively will also checkout the CyCADA fork of the CycleGAN repo).git clone –recursive […]

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Models

Official repository for Fourier model that can generate periodic signals

Jiyoung Lee, Wonjae Kim, Daehoon Gwak, Edward Choi This repository provides official pytorch implementation of Fourier-Based Decoder which can generate periodic signals. The paper can be found in this link: Conditional Generation of Periodic Signals with Fourier-Based Decoder Pytorch version >= 1.7.1 Python version >= 3.7 This repository is MIT-licensed. Please cite as: @inproceedings{lee2021conditional, title={Conditional Generation of Periodic Signals with Fourier-Based Decoder}, author={Lee, Jiyoung and Kim, Wonjae and Gwak, Daehoon and Choi, Edward}, booktitle={Advances in Neural Information Processing Systems},   […]

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Minecraft

MCRPC (Minecraft Resource Pack Comparator) checks your resource pack against any version of Minecraft

MCRPC checks your resource pack against any version of Minecraft to show resources missing from your pack for that version. Installation and usage Clone the repo git clone https://github.com/txtsd/mcrpc.git You will need Python Install the dependencies: pip install -r requirements.txt Or if you have pipenv (preferred), install the pipenv environment and dependencies: pipenv install pipenv shell Finally, run Follow the prompts, and you will get a list of missing resources. Screenshot

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Dataset

Triangulation Supports Agricultural Spread

How to cite If you use these data please cite Description This dataset is licensed under a CC-BY-4.0 license Statistics Varieties: 101 Concepts: 254 Lexemes: 26,224 Sources: 0 Synonymy: 1.08 Cognacy: 26,224 cognates in 3,173 cognate sets (812 singletons) Cognate Diversity: 0.11 Invalid lexemes: 0 Tokens: 115,799 Segments: 367 (0 BIPA errors, 0 CTLS sound class errors, 369 CLTS modified) Inventory    

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Neural Network

Plenoxels: Radiance Fields without Neural Networks

Alex Yu*, Sara Fridovich-Keil*, Matthew Tancik, Qinhong Chen, Benjamin Recht, Angjoo Kanazawa UC Berkeley Website and video: https://alexyu.net/plenoxels arXiv: https://arxiv.org/abs/2112.05131 Note: This JAX implementation is intended to be high-level and user-serviceable, but is much slower (roughly 1 hour per epoch) than the CUDA implementation https://github.com/sxyu/svox2 (roughly 1 minute per epoch), and there is not perfect feature alignment between the two versions. This JAX version can likely be sped up significantly, and we may push performance improvements and extra features in […]

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Cryptocurrency

Only works with the dashboard version / branch of jesse

Only works with the dashboard version / branch of jesse. The config.yml should be self-explainatory. # install from git pip install git+https://github.com/cryptocoinserver/jesse-optuna.git # cd in your Jesse project directory # create the config file jesse-optuna create-config # create the database for optuna jesse-optuna create-db optuna_db # edit the created yml file in your project directory # run jesse-optuna run

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Transformer

Embracing Single Stride 3D Object Detector with Sparse Transformer

This is the official implementation of paper: Embracing Single Stride 3D Object Detector with Sparse Transformer Authors:Lue Fan,Ziqi Pang,Tianyuan Zhang,Yu-Xiong Wang,Hang Zhao,Feng Wang,Naiyan Wang,Zhaoxiang Zhang Paper Link (Check again on Monday) Introduction and Highlights SST is a single-stride network, which maintains original feature resolution from the beginning to the end of the network. Due to the characterisric of single stride, SST achieves exciting performances on small object detection (Pedestrian, Cyclist). For simplicity, except for backbone, SST is almost the same […]

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