HubertStone

HubertStone's Stars

• first20hours/google-10000-english

  This repo contains a list of the 10,000 most common English words in order of frequency, as determined by n-gram frequency analysis of the Google's Trillion Word Corpus.

  3.9k106271.9k

• szagoruyko/pytorchviz

  A small package to create visualizations of PyTorch execution graphs

  Language:Jupyter Notebook3.2k3164279

• gordicaleksa/pytorch-GAT

  My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

  Language:Jupyter Notebook2.5k4614333

• baifanxxx/awesome-active-learning

  A curated list of awesome Active Learning

  73415569

• cerlymarco/shap-hypetune

  A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

  Language:Jupyter Notebook56773371

• online-ml/awesome-online-machine-learning

  :bookmark_tabs: Online machine learning resources

  50320155

• danieltan07/learning-to-reweight-examples

  PyTorch Implementation of the paper Learning to Reweight Examples for Robust Deep Learning

  Language:Jupyter Notebook353122160

• lyakaap/VAT-pytorch

  Virtual Adversarial Training (VAT) implementation for PyTorch

  Language:Python29651243

• xjtushujun/meta-weight-net

  NeurIPS'19: Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting (Pytorch implementation for noisy labels).

  Language:Python28271568

• uber-research/learning-to-reweight-examples

  Code for paper "Learning to Reweight Examples for Robust Deep Learning"

  Language:Python269101252

• gorkemalgan/deep_learning_with_noisy_labels_literature

  This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.

  23511231

• XinzeZhang/HUST-PhD-Thesis-Latex

  华中科技大学博士毕业论文Latex模板

  Language:TeX15142335

• LiJunnan1992/MLNT

  Meta-Learning based Noise-Tolerant Training

  Language:Python12351329

• microsoft/MLC

  Meta Label Correction for Noisy Label Learning

  Language:Python8251521

• ATayebi/GridFormingConverters

  Implementation of Grid-Forming Control Techniques in IEEE 9-Bus System

  Language:MATLAB714216

• NREL/PyPSCAD

  6812324

• Future-Power-Networks/Simplus-Grid-Tool

  Language:MATLAB654641

• cgnorthcutt/cleanlab

  Official cleanlab repo is at https://github.com/cleanlab/cleanlab

  57208

• TinfoilHat0/Learning-to-Reweight-Examples-for-Robust-Deep-Learning-with-PyTorch-Higher

  An implementation of the paper "Learning to Reweight Examples for Robust Deep Learning" from ICML 2018 with PyTorch and Higher.

  Language:Jupyter Notebook27334

• bpoolla/Australian10gen-lowinertia

  Implementation of Grid-Forming and Grid-Following Virtual Inertia

  Language:MATLAB262012

• Future-Power-Networks/Publications

  Language:MATLAB201015

• l2ep-epmlab/VSC_Lib

  Language:MATLAB201210

• ZhenWang-PhD/Training-Noise-Robust-Deep-Neural-Networks-via-Meta-Learning

  Code for CVPR2020 ‘Training Noise Robust Deep Neural Networks via Meta-Learning’

  Language:Python20102

• gorkemalgan/MSLG_noisy_label

  Official code for the paper "Meta Soft Label Generation for Noisy Labels" accepted at ICPR 2020.

  Language:Python18013

• kremerj/relabeling

  Code repository for the robust active label correction paper.

  Language:Terra10102

• maxscheijen/radar

  🎯 A simple Python package for creating radar charts.

  Language:Python10111

• YifanWu-97/PowerFactory-Grid-Forming

  This repository contains the PowerFactory models of the Grid-Forming and Grid-following model, as well as a 4-bus benchmark system.

  8101

• wxyhhh/tapnet

  TapNet: Multivariate Time Series Classification withAttentional Prototypical Network

  Language:Python61016

• fllseu/grid_forming

  5003

• krayush07/learn-by-weak-supervision

  A meta-learning setup to utitlize the unlabeled data for target task. An implementation of "Learning to learn from weak supervision by full supervision". https://arxiv.org/abs/1711.11383

  Language:Python4401