joekreatera

joekreatera's Stars

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.3k2201564.1k

• esp8266/Arduino

  ESP8266 core for Arduino

  Language:C++16k9515.9k13.3k

• Diyago/Tabular-data-generation

  We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

  Language:Python52694182

• gh0x0st/Buffer_Overflow

  Don't let buffer overflows overflow your mind

  Language:Python43191116

• JoaquinAmatRodrigo/Estadistica-con-R

  Apuntes personales sobre estadística, machine learning y lenguaje de programación R

  Language:R353341287

• LimelightVision/limelightlib-wpijava

  Language:Java48101115

• dbindel/sjtu-summer2018

  Course material for "Numerical Methods for Data Science" (SJTU, summer 2018)

  Language:TeX401419

• ndahn/Rocksi

  Rocksi - The Robot Blocks Simulator

  Language:JavaScript25277

• fcharte/SM-MLC

  Multilabel Classification - Problem analysis, metrics and techniques software and data repository

  Language:Java24505

• wanglichenxj/Dual-Relation-Semi-supervised-Multi-label-Learning

  Language:Python24315

• daiquanyu/AdaGCN_TKDE

  This paper studies the problem of cross-network node classification to overcome the insufficiency of labeled data in a single network. It aims to leverage the label information in a partially labeled source network to assist node classification in a completely unlabeled or partially labeled target network. Existing methods for single network learning cannot solve this problem due to the domain shift across networks. Some multi-network learning methods heavily rely on the existence of cross-network connections, thus are inapplicable for this problem. To tackle this problem, we propose a novel graph transfer learning framework AdaGCN by leveraging the techniques of adversarial domain adaptation and graph convolution. It consists of two components: a semi-supervised learning component and an adversarial domain adaptation component. The former aims to learn class discriminative node representations with given label information of the source and target networks, while the latter contributes to mitigating the distribution divergence between the source and target domains to facilitate knowledge transfer. Extensive empirical evaluations on real-world datasets show that AdaGCN can successfully transfer class information with a low label rate on the source network and a substantial divergence between the source and target domains.

  Language:Python23102

• bbenligiray/rml-cnn

  Semi-Supervised Robust Deep Neural Networks for Multi-Label Classification

  Language:Python17332

• juantoman/MyClassGame

  Language:HTML11442

• Tsingzao/Semi_Supervised_Multi_Label_Learning

  Language:M7203

• Opentrons/covid19-system-9

  Language:Python3901

• jrlangford/bank_ddd_demo

  Language:Python2200

• justcallmewilliam/PMvC

  Matlab implementation of 'Pseudo-label Guided Multi-view Consensus Graph Learning for Semi-Supervised Classification', IJIS

  2120

• irina-lebedeva/Curriculum-Labeling

  semi-supervised on multi-ethnic fbp dataset

  Language:Jupyter Notebook1100

• joekreatera/OOPSummer

  ITESM Summer Object Oriented Programming Course

  Language:Java10

• richadhanuka/PFP-Autoencoders

  Language:Python110

• ytaek-oh/mlssl-simple

  Simple codebase for multi-label semi-supervised learning algorithms

  Language:Python1100