vaseline555

AAggFF

(ICML 2024) Pursuing Overall Welfare in Federated Learning through Sequential Decision Making (https://arxiv.org/abs/2402.14650)

Algorithms-for-Optimization-Python

Unofficial implementation in Python porting of the book "Algorithms for Optimization" (2019) MIT Press by By Mykel J. Kochenderfer and Tim A. Wheeler

Blockchain-based-Electronic-Lab-Notebook

Course project of MGE450 Project Lab, UNIST (Hyperledger Composer)

Classifying-Brain-Signal-of-Pain

Course project of MGE304 Management Engineering Methodologies, UNIST

Federated-Learning-in-PyTorch

Handy PyTorch implementation of Federated Learning (for your painless research)

Federated-Learning-with-Flower

Simple simulation of federated learning (FedAvg) using Flower framework

Model-Agnostic-Meta-Learning-PyTorch

Model Agnostic Meta Learning (Finn et al., 2017; MAML) implemented in PyTorch

Prototypical-Networks-PyTorch

Prototypical Networks (Snell et al., 2017; ProtoNet) implemented in PyTorch

SuPerFed

(SIGKDD 2022) Connected Low-Loss Subspace Learning for a Personalization in Federated Learning (https://arxiv.org/abs/2109.07628)

T2D-Predictive-Modeling-for-Korean-with-gPRS-and-Metabolites

Source code of the paper, "Prediction of Type 2 Diabetes using Genome-wide Polygenic Risk Score and Metabolic Profiles: A Machine Learning Analysis of Population-based 10-year Prospective Cohort Study".

vaseline555/Federated-Learning-in-PyTorch

Handy PyTorch implementation of Federated Learning (for your painless research)

vaseline555/Algorithms-for-Optimization-Python

Unofficial implementation in Python porting of the book "Algorithms for Optimization" (2019) MIT Press by By Mykel J. Kochenderfer and Tim A. Wheeler

vaseline555/SuPerFed

(SIGKDD 2022) Connected Low-Loss Subspace Learning for a Personalization in Federated Learning (https://arxiv.org/abs/2109.07628)

vaseline555/Federated-Learning-with-Flower

Simple simulation of federated learning (FedAvg) using Flower framework

vaseline555/AAggFF

(ICML 2024) Pursuing Overall Welfare in Federated Learning through Sequential Decision Making (https://arxiv.org/abs/2402.14650)

vaseline555/Prototypical-Networks-PyTorch

Prototypical Networks (Snell et al., 2017; ProtoNet) implemented in PyTorch

vaseline555/Model-Agnostic-Meta-Learning-PyTorch

Model Agnostic Meta Learning (Finn et al., 2017; MAML) implemented in PyTorch

vaseline555/Blockchain-based-Electronic-Lab-Notebook

Course project of MGE450 Project Lab, UNIST (Hyperledger Composer)

vaseline555/T2D-Predictive-Modeling-for-Korean-with-gPRS-and-Metabolites

Source code of the paper, "Prediction of Type 2 Diabetes using Genome-wide Polygenic Risk Score and Metabolic Profiles: A Machine Learning Analysis of Population-based 10-year Prospective Cohort Study".

vaseline555/Classifying-Brain-Signal-of-Pain

Course project of MGE304 Management Engineering Methodologies, UNIST

vaseline555/Least-Squares-Audio-Processing

Course project of MGE201 Operations Research I, UNIST

vaseline555/Object-Oriented-Programming

Course works of CSE241 Object Oriented Programming, UNIST

vaseline555/vaseline555.github.io

vaseline555/vert-papers

This repository contains code and datasets related to entity/knowledge papers from the VERT (Versatile Entity Recognition & disambiguation Toolkit) project, by the Knowledge Computing group at Microsoft Research Asia (MSRA).

vaseline555/Yelp-Restaurant-Photo-Classification

Past Kaggle competition : Yelp Restaurant Photo Classification

vaseline555/Course_cs211-data-privacy

vaseline555/Few-shot-NER

Code and data of ACL 2021 paper "Few-NERD: A Few-shot Named Entity Recognition Dataset"