Ryan (YanZehong)

YanZehong

Geek Repo

Company:Harbin Institute of Technology, Shenzhen

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Ryan's repositories

yanzehong.github.io

Build a Jekyll blog in minutes, without touching the command line.

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cs6101

The Web IR / NLP Group (WING)'s public reading group at the National University of Singapore.

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CS5242-ABSC

NUS CS5242 A+ project

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CS5228-404

NUS CS5228 A+ project

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albumentations

Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

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lightning

Build and train PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, and other headaches.

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lightning-hydra-template

PyTorch Lightning + Hydra. A very user-friendly template for rapid and reproducible ML experimentation with best practices. ⚡🔥⚡

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segmentation_models.pytorch

Segmentation models with pretrained backbones. PyTorch.

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bert

TensorFlow code and pre-trained models for BERT

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all-in-one

[Arxiv2022] All in One: Exploring Unified Video-Language Pre-training

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grouptogether_IT5007proj

IT5007 group project

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ABSA-Reading-List

Reading list of aspect-based sentiment analysis.

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autogluon

AutoGluon: AutoML for Image, Text, and Tabular Data

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ACE

[ACL-IJCNLP 2021] Automated Concatenation of Embeddings for Structured Prediction

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next-auth-example

Example showing how to use NextAuth.js with Next.js

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ABSA-PyTorch

Aspect Based Sentiment Analysis, PyTorch Implementations. 基于方面的情感分析,使用PyTorch实现。

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LCFS-BERT

The Local Context Focus Syntax-based BERT model to analyze aspect-based sentiment of product reviews on SemEval2014

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pro-mern-stack-2

Code listing for the book Pro MERN Stack, 2nd Edition

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FINDER

FINDER - FInding key players in complex Networks through DEep Reinforcement learning (Nature Machine Intelligence)

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Temporal-Component-Weight

A realtime vehicle status perception approach, which directly extracts vehicle status from our proposed novel video feature, temporal component-weight. Specifically, temporal component-weight is calculated based on a sampling of the whole frame.

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