Hyunjoon526

Hyunjoon526's Stars

• Blealtan/efficient-kan

  An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

  Language:Python4.2k377

• KindXiaoming/pykan

  Kolmogorov Arnold Networks

  Language:Jupyter Notebook15.3k1.4k

• thuml/Nonstationary_Transformers

  Code release for "Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting" (NeurIPS 2022), https://arxiv.org/abs/2205.14415

  Language:Python49285

• SJTU-DMTai/MASTER

  This is the official code and supplementary materials for our AAAI-2024 paper: MASTER: Market-Guided Stock Transformer for Stock Price Forecasting. MASTER is a stock transformer for stock price forecasting, which models the momentary and cross-time stock correlation and guide feature selection with market information.

  Language:Python22743

• AI4Finance-Foundation/FinRL

  FinRL: Financial Reinforcement Learning. 🔥

  Language:Jupyter Notebook10.5k2.5k

• zzsza/Datascience-Interview-Questions

  Datascience-Interview-Questions for Korean

  897222

• THUwangcy/ReChorus

  “Chorus” of recommendation models: a light and flexible PyTorch framework for Top-K recommendation.

  Language:Python56497

• maziarraissi/Applied-Deep-Learning

  Applied Deep Learning Course

  3.3k688

• SeongBeomLEE/RecsysTutorial

  추천시스템 논문을 읽고 구현한 Code가 저장된 Repository

  Language:Jupyter Notebook6711

• muhanzhang/IGMC

  Inductive graph-based matrix completion (IGMC) from "M. Zhang and Y. Chen, Inductive Matrix Completion Based on Graph Neural Networks, ICLR 2020 spotlight".

  Language:Python35883

• yuqinie98/PatchTST

  An offical implementation of PatchTST: "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers." (ICLR 2023) https://arxiv.org/abs/2211.14730

  Language:Python1.8k302

• JordiCorbilla/stock-prediction-deep-neural-learning

  Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for times series forecasting

  Language:Jupyter Notebook540115

• zzw-zwzhang/Awesome-of-Time-Series-Prediction

  A curated list of time series prediction resources.

  549

• YangLinyi/FinNLP-Progress

  NLP progress in Fintech. A repository to track the progress in Natural Language Processing (NLP) related to the domain of Finance, including the datasets, papers, and current state-of-the-art results for the most popular tasks.

  37458

• GXimingLu/a_star_neurologic

  Language:Python435

• Eric-mingjie/rethinking-network-pruning

  Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)

  Language:Python1.5k290

• rahulvigneswaran/Lottery-Ticket-Hypothesis-in-Pytorch

  This repository contains a Pytorch implementation of the paper "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" by Jonathan Frankle and Michael Carbin that can be easily adapted to any model/dataset.

  Language:Python32892

• JanSchm/CapMarket

  Language:Jupyter Notebook278170

• chaitjo/efficient-gnns

  Code and resources on scalable and efficient Graph Neural Networks

  Language:Python52864

• memoiry/Awesome-model-compression-and-acceleration

  663123

• Tencent/PocketFlow

  An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

  Language:Python2.8k488

• cedrickchee/awesome-ml-model-compression

  Awesome machine learning model compression research papers, quantization, tools, and learning material.

  50161

• chester256/Model-Compression-Papers

  Papers for deep neural network compression and acceleration

  39780

• ChanChiChoi/awesome-model-compression

  papers about model compression

  16638

• j-marple-dev/model_compression

  PyTorch Model Compression

  Language:Python23025

• juliagusak/model-compression-and-acceleration-progress

  Repository to track the progress in model compression and acceleration

  10520

• SNUCSE-CTA/IDAR

  Fast Supergraph Search Using DAG Integration

  Language:Shell107

• thunlp/GNNPapers

  Must-read papers on graph neural networks (GNN)

  16.2k3k

• sproogen/modern-resume-theme

  A modern static resume template and theme. Powered by Jekyll and GitHub pages.

  Language:HTML2.1k1.4k