mzalaya

mzalaya's Stars

• IBM/HOTT

  Code for NeurIPS 2019 paper "Hierarchical Optimal Transport for Document Representation"

  Language:Python5421

• judelo/gmmot

  Python notebooks for Optimal Transport between Gaussian Mixture Models

  Language:Jupyter Notebook419

• evarol/ot_tracking

  tracking objects using optimal transport

  Language:Jupyter Notebook21

• PengBoXiangShang/Stochastic_Generative_Hashing

  Language:Jupyter Notebook2

• PengBoXiangShang/awesome-meta-learning

  A curated list of Meta-Learning resources/papers.

  1

• jindongwang/transferlearning

  Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

  Language:Python13.6k3.8k

• JihongJu/deep-pu-learning

  Deep Learning with Positive and Unlabeled samples

  Language:Jupyter Notebook548

• rasbt/deeplearning-models

  A collection of various deep learning architectures, models, and tips

  Language:Jupyter Notebook16.8k4k

• jvanvugt/pytorch-domain-adaptation

  A collection of implementations of adversarial domain adaptation algorithms

  Language:Python620108

• tensorflow/models

  Models and examples built with TensorFlow

  Language:Python77.3k45.7k

• pumpikano/tf-dann

  Domain-Adversarial Neural Network in Tensorflow

  Language:Jupyter Notebook630223

• fungtion/DANN

  pytorch implementation of Domain-Adversarial Training of Neural Networks

  Language:Python857156

• maelfabien/Machine_Learning_Tutorials

  Code, exercises and tutorials of my personal blog ! 📝

  Language:Jupyter Notebook780258

• thomalm/svhn-multi-digit

  Language:Jupyter Notebook6221

• eriklindernoren/PyTorch-GAN

  PyTorch implementations of Generative Adversarial Networks.

  Language:Python16.6k4.1k

• MorvanZhou/Tensorflow-Tutorial

  Tensorflow tutorial from basic to hard, 莫烦Python 中文AI教学

  Language:Python4.3k1.9k

• MorvanZhou/PyTorch-Tutorial

  Build your neural network easy and fast, 莫烦Python中文教学

  Language:Jupyter Notebook8.2k3.1k

• wogong/pytorch-dann

  A PyTorch implementation for Unsupervised Domain Adaptation by Backpropagation

  Language:Jupyter Notebook14819

• fastai/word-embeddings-workshop

  This contains materials for the word embeddings workshop

  Language:HTML12567

• kudkudak/word-embeddings-benchmarks

  Package for evaluating word embeddings

  Language:Python436110

• kiryor/nnPUlearning

  Non-negative Positive-Unlabeled (nnPU) and unbiased Positive-Unlabeled (uPU) learning reproductive code on MNIST and CIFAR10

  Language:Python29764

• IssamLaradji/M-ADDA

  Domain Adaptation Based on the Triplet Loss

  Language:Python11327

• ML-KULeuven/SAR-PU

  Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data

  Language:Jupyter Notebook345

• MasaKat0/PUlearning

  Code for Positive-Unlabeled learning.

  Language:Python3515

• mblondel/smooth-ot

  Python implementation of smooth optimal transport.

  Language:Python5713

• quanmingyao/AIS-Impute-Tensor-

  Matlab Code for Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. IEEE Transactions on Knowledge and Data Engineering (TKDE). 2018.

  Language:Matlab18

• francoispierrepaty/SubspaceRobustWasserstein

  Source code for the ICML2019 paper "Subspace Robust Wasserstein Distances"

  Language:Jupyter Notebook283

• davidstutz/ipiano

  Implementation of the iPiano algorithm for non-convex and non-smooth optimization as described in [1].

  Language:C++133

• illidanlab/Non-convex-solver-iPiano

  Language:Matlab41

• HongtengXu/s-gwl

  Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching

  Language:Python4110