MyeongHaHwang

2019DeepLearning_IE

DeepLearning_Lecture 2019 in Industrial Engineering at KAIST

AMCF

Explainable recommendation via interpretable feature mapping

autoencoding_vi_for_topic_models

Tensorflow implementation for prodLDA and NVLDA.

AutoGPT

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

KLUE

📖 Korean NLU Benchmark

multilingual-t5

py-hanspell

파이썬 한글 맞춤법 검사 라이브러리. (네이버 맞춤법 검사기 사용)

Synbi-Side

Synbi-Side: Drug Side Effect Prediction Framework based on Multi-feature Deep Learning for Synergistic Bioinformatics

torchrec

Pytorch domain library for recommendation systems

ViLT

Code for the ICML 2021 (long talk) paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

MyeongHaHwang/KLUE

📖 Korean NLU Benchmark

MyeongHaHwang/multilingual-t5

MyeongHaHwang/Synbi-Side

Synbi-Side: Drug Side Effect Prediction Framework based on Multi-feature Deep Learning for Synergistic Bioinformatics

MyeongHaHwang/torchrec

Pytorch domain library for recommendation systems

MyeongHaHwang/ViLT

Code for the ICML 2021 (long talk) paper: "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision"

MyeongHaHwang/autoencoding_vi_for_topic_models

Tensorflow implementation for prodLDA and NVLDA.

MyeongHaHwang/AutoGPT

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

MyeongHaHwang/BERTopic

Leveraging BERT and c-TF-IDF to create easily interpretable topics.

MyeongHaHwang/DeepAligned-Clustering

Discovering New Intents with Deep Aligned Clustering (AAAI 2021)

MyeongHaHwang/DeepCTR-Torch

【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

MyeongHaHwang/DRKG

A knowledge graph and a set of tools for drug repurposing

MyeongHaHwang/DrugMecha

Database

MyeongHaHwang/DrugMechDB

A database of paths that represent the mechanism of action from a drug to a disease in an indication.

MyeongHaHwang/DSGAT

Here we propose a novel end-to-end approach named DSGAT for drug-side effect frequency prediction by using graph attention networks (GAT) . DSGAT learns the drug representation from the molecular graph, and has the ability to predict the frequencies of side effects for cold start drugs that do not appear in the training data.

MyeongHaHwang/dstc11-track2-intent-induction

DSTC 11 Track 2: Intent Induction from Conversations for Task-Oriented Dialogue

MyeongHaHwang/fastcampus-RecSys

2020 패스트캠퍼스 추천시스템 A to Z

MyeongHaHwang/GraphGPT

Extrapolating knowledge graphs from unstructured text using GPT-3 🕵️‍♂️

MyeongHaHwang/hetionet

Hetionet: an integrative network of disease

MyeongHaHwang/Informer2020

The GitHub repository for the paper "Informer" accepted by AAAI 2021.

MyeongHaHwang/KoAlpaca

KoAlpaca: Korean Alpaca Model based on Stanford Alpaca (feat. LLAMA and Polyglot-ko)

MyeongHaHwang/LDA_scRNAseq

Source codes and example data for the LDA-based scRNA-seq analysis workflow

MyeongHaHwang/llama

Inference code for LLaMA models

MyeongHaHwang/llama3

The official Meta Llama 3 GitHub site

MyeongHaHwang/llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

MyeongHaHwang/neo4j

Graphs for Everyone

MyeongHaHwang/neural_collaborative_filtering

Neural Collaborative Filtering

MyeongHaHwang/nqg

neural question generation for reading comprehension

MyeongHaHwang/question_generation

Neural question generation using transformers

MyeongHaHwang/recommenders

Best Practices on Recommendation Systems

MyeongHaHwang/vega

VEGA: VAE Enhanced by Gene Annotations