Browse SoTA > Methodology > Clustering

# Clustering Edit

1092 papers with code · Methodology

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# TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

14 Mar 2016tensorflow/tensorflow

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.

150,809

# Automatic Differentiation in PyTorch

In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.

44,195

# Scikit-learn: Machine Learning in Python

2 Jan 2012scikit-learn/scikit-learn

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.

43,168

# Caffe: Convolutional Architecture for Fast Feature Embedding

20 Jun 2014BVLC/caffe

The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.

31,086

# XGBoost: A Scalable Tree Boosting System

9 Mar 2016dmlc/xgboost

In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results on many machine learning challenges.

20,181

# MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems

3 Dec 2015apache/incubator-mxnet

This paper describes both the API design and the system implementation of MXNet, and explains how embedding of both symbolic expression and tensor operation is handled in a unified fashion.

19,121

# CNTK: Microsoft's Open-Source Deep-Learning Toolkit

This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux.

16,917

# Fairness in Streaming Submodular Maximization: Algorithms and Hardness

Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.

14,288

# Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

Ranked #1 on Node Classification on Pubmed (F1 metric)

14,288

# Fair Correlation Clustering

We define a fairlet decomposition with cost similar to the $k$-median cost and this allows us to obtain approximation algorithms for a wide range of fairness constraints.