Clustering
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Greatest papers with code
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
Automatic Differentiation in PyTorch
NIPS 2017 2017
• pytorch/pytorch
• 
In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models.
Scikit-learn: Machine Learning in Python
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.
Caffe: Convolutional Architecture for Fast Feature Embedding
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.
XGBoost: A Scalable Tree Boosting System
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.
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems
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.
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
ACM SIGKDD 2016
• Microsoft/CNTK
• 
This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux.
Fairness in Streaming Submodular Maximization: Algorithms and Hardness
NeurIPS 2020
• google-research/google-research
•
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
KDD 2019
• google-research/google-research
•
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].
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CLUSTERING GRAPH CLUSTERING LINK PREDICTION NODE CLASSIFICATION
Fair Correlation Clustering
• google-research/google-research
•
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.