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Deep Clustering Evaluation: How to Validate Internal Clustering Validation Measures
Two key issues are identified: 1) the curse of dimensionality when applying these measures to raw data, and 2) the unreliable comparison of clustering results across different embedding spaces stemming from variations in training procedures and parameter settings in different clustering models.
Locally Adaptive and Differentiable Regression
Over-parameterized models like deep nets and random forests have become very popular in machine learning.
The ART of Transfer Learning: An Adaptive and Robust Pipeline
Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources.
Meta Clustering for Collaborative Learning
We propose a framework named meta clustering to address the challenge.
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