Kernelized Infomax Clustering

By Felix V. Agakov, David Barber. 2005

38 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Pieter Abbeel
2. Sergey Levine
3. John Schulman
4. Yan Duan
5. Chelsea Finn
6. Xi Chen
7. Ilya Sutskever
8. Bradly C. Stadie

Citations in the corpus

• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets (2016, methodology): “More formally, the minimax game is given by the following expression: min G max D V (D,G) = Ex∼Pdata [logD(x)] + Ez∼noise[log (1−D(G(z)))] (1)”
• Third-Person Imitation Learning (2017, background): “(See Bridle et al. (1992); Barber & Agakov (2005); Krause et al. (2010); Chen et al. (2016) for further discussion on instantiating the information term by introducing another classifier.)”
• Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization (2017, background): “Information theoretic [18, 2, 14], maxmargin [64, 55], and spectral graph [24] algorithms are examples of discriminative clustering models.”