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fffaby's Projects

hyphc icon hyphc

Hyperbolic Hierarchical Clustering.

kmedoids icon kmedoids

The Partitioning Around Medoids (PAM) implementation of K-Medoids algorithm in Python.

kmedoids-1 icon kmedoids-1

[Unmaintained] The Python implementation of k-medoids.

leeml-notes icon leeml-notes

李宏毅《机器学习》笔记,在线阅读地址:https://datawhalechina.github.io/leeml-notes

linearcorex icon linearcorex

Fast, linear version of CorEx for covariance estimation, dimensionality reduction, and subspace clustering with very under-sampled, high-dimensional data

machine-learning icon machine-learning

:zap:机器学习实战(Python3):kNN、决策树、贝叶斯、逻辑回归、SVM、线性回归、树回归

modelarts-lab icon modelarts-lab

ModelArts-Lab是示例代码库。更多AI开发学习交流信息,请访问华为云AI开发者社区:https://developer.huaweicloud.com/techfield/ai.html

mom icon mom

Mining on Manifolds (CVPR 2018)

netnmf-sc icon netnmf-sc

netNMF-sc: A network regularization algorithm for dimensionality reduction and imputation of single-cell expression data

pamae icon pamae

使用Python复现SIGKDD2017的PAMAE算法(并行k-medoids算法)/The Python implementation of SIGKDD 2017's PAMAE algorithm (parallel k-medoids algorithm)

pica icon pica

Official Pytorch Implementation for CVPR'20 paper: Deep Semantic Clustering by Partition Confidence Maximisation

pt-dec icon pt-dec

PyTorch implementation of DEC (Deep Embedding Clustering)

pvae icon pvae

code for "Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders".

pytorch-book icon pytorch-book

PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation (《深度学习框架PyTorch:入门与实战》)

pytorch-metric-learning icon pytorch-metric-learning

The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

s-vae-pytorch icon s-vae-pytorch

Pytorch implementation of Hyperspherical Variational Auto-Encoders

spbl_pytorch icon spbl_pytorch

PyTorch implementation of "Self-Paced Balance Learning for Clinical Skin Disease Recognition"

spl-advise icon spl-advise

PyTorch code for BMVC 2018 paper: "Self-Paced Learning with Adaptive Visual Embeddings"

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