- Dept. of Statistics, University of Seoul
- Variational AutoEncoder
- Synthetic Data Generation
- Optimization
- Causal Inference
Name: an-seunghwan
Type: User
Company: Statistical Data Science, University of Seoul
Bio: VAE, Synthetic Data Generation & Optimization / Statistics, University Of Seoul
Location: Seoul, Korea
an-seunghwan's blog
Implementation PyTorch codes for causal discovery
Official pytorch implementation codes for ECAI-2023 accepted paper "Causally Disentangled Generative Variational AutoEncoder"
Algorithms for fit constrained lasso
Official pytorch implementation codes for NeurIPS-2023 accepted paper "Distributional Learning of Variational AutoEncoder: Application to Synthetic Data Generation"
A Korean NLP Python Library for Economic Analysis
Official tensorflow implementation codes for Pattern Recognition Letters accepted paper "Customization of latent space in semi-supervised variational autoencoder"
Reproductive implementation Tensorflow 2.0 codes for Generative modeling
A Sass converter for Jekyll.
기존 한국어 전처리 라이브러리인 koynlp 와 sonlpy 의 장점만 섞은 Library 입니다.
:triangular_ruler: Jekyll theme for building a personal site, blog, project documentation, or portfolio.
MPI programming lessons in C and executable code examples
2018-2 창의공학기초설계 수업
Implementation codes for various semi-supervised learning methods.
한국어 자연어처리를 위한 파이썬 라이브러리입니다. 단어 추출/ 토크나이저 / 품사판별/ 전처리의 기능을 제공합니다.
Implementations of various synthesizers with pytorch.
텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다
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