K-means Clustering

3 Posts

Graph with difference in test error in keeping hard versus easy examples
K-means Clustering

Unsupervised Data Pruning: New method removes useless machine learning data.

Large datasets often contain overly similar examples that consume training cycles without contributing to learning. A new paper identifies similar training examples, even if they’re not labeled.
Illustration of group of faceless people and two people with visible faces
K-means Clustering

K-Means Clustering: Group Think - K-Means Clustering for Machine Learning Explained

If you’re standing close to others at a party, it’s likely you have something in common. This is the idea behind using k-means clustering to split data points into groups. Whether the groups formed via human agency or some other force, this algorithm will find them.
Information related to the kNN-LM algorithm
K-means Clustering

Helpful Neighbors: A research summary of the kNN-LM language model

School teachers may not like to hear this, but sometimes you get the best answer by peeking at your neighbor’s paper. A new language model framework peeks at the training data for context when making a prediction.

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