Multi-modal estimation with kernel embeddings for learning motion models

By Lachlan McCalman, Simon Timothy O'Callaghan, Fabio Tozeto Ramos. 2013

26 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Multimodal Learning, 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 Aerospace Engineering

1. Kenji Fukumizu
2. Bernhard Schölkopf
3. Motonobu Kanagawa
4. Bharath K. Sriperumbudur

In Computer Vision and Pattern Recognition

1. Bharath K. Sriperumbudur
2. Kenji Fukumizu
3. Krikamol Muandet
4. Bernhard Schölkopf
5. Motonobu Kanagawa
6. Luc Van Gool
7. David Lopez-Paz
8. Arthur Gretton

In Multimodal Learning

1. Kenji Fukumizu
2. Motonobu Kanagawa

In Representation & Self-Supervised Learning

1. Bharath K. Sriperumbudur
2. Bernhard Schölkopf
3. Kenji Fukumizu
4. Krikamol Muandet
5. Motonobu Kanagawa
6. Arthur Gretton
7. David Lopez-Paz
8. Kun Zhang

Citations in the corpus

• Kernel Mean Embedding of Distributions: A Review and Beyonds (2016, methodology): “In a state-space model, for example, we typically obtain a kernel mean estimate of the predictive distribution from the algorithm (Song et al. 2009, Nishiyama et al. 2012, McCalman et al. 2013).”
• Recovering Distributions from Gaussian RKHS Embeddings (2014, methodology): “On the other hand, a method for estimating the density of P from m̂P has been proposed, assuming that P is a Gaussian mixture (Song et al., 2008; McCalman et al., 2013).”