PyTorch

The cross-modality generative model that synthesizes dance from music

Dancing to Music PyTorch implementation of the cross-modality generative model that synthesizes dance from music. Paper Hsin-Ying Lee, Xiaodong Yang, Ming-Yu Liu, Ting-Chun Wang, Yu-Ding Lu, Ming-Hsuan Yang, Jan KautzDancing to Music Neural Information Processing Systems (NeurIPS) 2019[Paper] [YouTube] [Project] [Blog] [Supp] Example Videos Beat-Matching1st row: generated dance sequences, 2nd row: music beats, 3rd row: kinematics beats MultimodalityGenerate various dance sequences with    

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Assistant

A Virtual Desktop Assistant Written in Python

A Virtual Desktop Assistant Written in Python. It’s generally a basic virtual assistant The basic purpose of this is to make work easier as it re-directs you to various main sites and performs various important functions for your PC as well just install it for your system and run it in your code editor or IDE. I will be soon updating it as an application for MacOS, Linux and Windows. Until then you can follow the Contributing Guidelines and Contribute […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 88: Text Segmentation and Multilinguality

With the semester start, it is also time to renew MT Weekly. My new year’s resolution was to make it to 100 issues, so let’s see if I can keep it. Today, I will talk about a paper by my colleagues from LMU Munich that will appear in the Findings of EMNLP 2021 which deals with a perpetual problem of NLP – input text segmentation. The title of the paper is Wine is Not v i n. On the Compatibility […]

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Data Analysis

Exploratory Data Analysis for Employee Retention Dataset

Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months. It is only natural then that data science has started being applied to this area. Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well […]

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pyqt5

Different Shapes Made With PyQt5

Different Shapes Made With PyQt5 How to contribute? Your pull request must contain a file with the name of the shape’s. Moreover, your python file should be independent, meaning that it should have an instance of QApplication created GitHub https://github.com/Win-tharsh/PyQt6-Shapes    

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hacktoberfest

A Hacktoberfest 2021 Python Repository

This is A Hacktoberfest 2021 Repository What You Have to do?? Fork This Repository Add Your Python Project With Name [YOUR PROJECT NAME.with extension of your language] Make A Pull Request and Wait For it To be Reviewed And Accepted if That Was Worth It! Note — Just Please Add the Project in Their Respective Folders For Example – If it is a python project, Then add it to python/your project.pyIf There is no Folder of your Language, Then you […]

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Command-line Tools

A CLI tool that can download songs from youtube

Music Downloader is a tool that can download songs from Youtube. Installation Base requirements: If you have Python 3.7+ installed, all you need to do is follow these steps: > git clone https://github.com/matjsilva/music-downloader> cd music-downloader> python install.py Then, you just need to: > python musicDownloader.py When the tool starts, drop a “help” on the input and enjoy Music Downloader. ⚙️ Technologies used GitHub https://github.com/matjsilva/music-downloader    

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API

An extremely basic API client for the Wyscout API for Python

wyscoutapi is an extremely basic API client for the Wyscout API (v2 & v3). Usage Install with pip install wyscoutapi. To connect to the Wyscout v3 api: import wyscoutapi client = wyscoutapi.WyscoutAPI( username=’myusername’, password=’mypassword’, ) client.player(329061) To use the v2 legacy API, or alter the rate-limit(defaults to 12 requests per second): import wyscoutapi client = wyscoutapi.WyscoutAPI( username=’myusername’, password=’mypassword’,

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Robust

Robust fine-tuning of zero-shot models

This repository contains code for the paper Robust fine-tuning of zero-shot models by Mitchell Wortsman*, Gabriel Ilharco*, Mike Li, Jong Wook Kim, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt. Abstract Large pre-trained models such as CLIP offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning approaches substantially improve accuracy in-distribution, they also reduce out-of-distribution robustness. We address this tension by introducing a simple and effective […]

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