jin-zhe

Jin Zhe's starred repositories

json_repair

A python module to repair invalid JSON, commonly used to parse the output of LLMs

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buddhistsutras

Mahayana Buddhist Sutras with Pinyin in HTML, Plain Text and PDF Format

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:bouncing_ball_person: Pytorch ReID: A tiny, friendly, strong pytorch implement of person re-id / vehicle re-id baseline. Tutorial 👉https://github.com/layumi/Person_reID_baseline_pytorch/tree/master/tutorial

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cs-video-courses

List of Computer Science courses with video lectures.

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thefuck

Magnificent app which corrects your previous console command.

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wacom-driver-fix

Fixes the Wacom Bamboo, Graphire, Intuos 1+2+3 and Cintiq 1st gen tablet drivers for macOS Catalina, Big Sur, Monterey (including M1 macs)

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CloserLookFewShot

source code to ICLR'19, 'A Closer Look at Few-shot Classification'

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nitrain

Train AI models efficiently on medical images using any framework

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gitignore

A collection of useful .gitignore templates

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ai-deadlines

:alarm_clock: AI conference deadline countdowns

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dispflownet-tf

Tensorflow implementation of https://lmb.informatik.uni-freiburg.de/Publications/2016/MIFDB16 + pretrained weights + implementation of "Unsupervised Adaptation for Deep Stereo" (ICCV 2017)

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ai_notes

machine learning/artificial intelligence notes

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ACAM_Demo

Real-time Action detection demo for the work Actor Conditioned Attention Maps. This repo includes a complete pipeline for person detection/tracking and analyzing their actions in real-time.

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MUNIT

Multimodal Unsupervised Image-to-Image Translation

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ICWSM18_LB2M

Dataset for ICWSM'18 Paper.

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CIKM_CUP

CIKM Cup 2016 (1st Place) - Track 1 - Cross Device Entity Linking :smile:

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pytorch_sentiment_rnn

Example Recurrent Neural Networks for Sentiment Analysis (Aspect-Based) on SemEval 2014

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KDD2018_MPCN

Code for our KDD 2018 Paper "Multi-Pointer Co-Attention Networks for Recommendation"

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WSDM2018_HyperQA

Reference Implementation for WSDM 2018 Paper "Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering"

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ava-dataset

The AVA dataset densely annotates 80 atomic visual actions in 351k movie clips with actions localized in space and time, resulting in 1.65M action labels with multiple labels per human occurring frequently.

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