Optimal kernels for unsupervised learning

By Sepp Hochreiter, Klaus Obermayer. 2005

5 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Representation & Self-Supervised Learning, Statistical Learning Theory, Signal Processing

How researchers see this paper

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

In Analytical Chemistry

1. Thomas Unterthiner
2. Sepp Hochreiter

In Artificial Intelligence

1. Sepp Hochreiter
2. Günter Klambauer
3. Thomas Unterthiner
4. Hubert Ramsauer
5. Bernhard Nessler
6. Martin Heusel
7. Quoc V. Le
8. Kevin Gimpel

In Representation & Self-Supervised Learning

1. Thomas Unterthiner
2. Günter Klambauer
3. Sepp Hochreiter
4. Bernhard Nessler
5. J. Urgen Schmidhuber

In Statistical Learning Theory

1. Hubert Ramsauer
2. Günter Klambauer
3. Martin Heusel
4. Thomas Unterthiner
5. Bernhard Nessler
6. Sepp Hochreiter

In Signal Processing

1. Hubert Ramsauer
2. Martin Heusel
3. Thomas Unterthiner
4. Sepp Hochreiter
5. Bernhard Nessler

Citations in the corpus

• Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields (2017, methodology): “Indeed only for particular kernels k gradient optimization of Φ( a ) leads to ρ ( b ) = 0 for all b , that is, p x ( b ) = p y ( b ) for all b [23] (see also Theorem 1 below).”