One-step neural network inversion with PDF learning and emulation

By Leemon Baird, David Smalenberger, Shawn Ingkiriwang. 2005

18 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

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

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Dacheng Tao
3. Ming-Hsuan Yang
4. Trevor Darrell
5. Jitendra Malik
6. Xiaogang Wang
7. Yoshua Bengio
8. Andrew Zisserman

Citations in the corpus

• Variational Inference with Normalizing Flows (2015, methodology): “, invertible neural networks (Baird et al., 2005; Rippel & Adams, 2013), such approaches typically have a complexity for computing the Jacobian determinant that scales as O(LD(3)), where D is the dimension of the hidden layers and L is the number of hidden layers used.”
• A RAD approach to deep mixture models (2019, background): “…variational autoencoders (Kingma & Welling, 2014; Rezende et al., 2014), generative adversarial networks (Goodfellow et al., 2014), and flow based models (Baird et al., 2005; Tabak & Turner, 2013; Dinh et al., 2015; 2017; Kingma & Dhari-wal, 2018; Chen et al., 2018; Grathwohl et al., 2019).”