A Machine Learning Perspective on Predictive Coding with PAQ8
By Byron Knoll, Nando de Freitas. 2012
34 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information 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 Artificial Intelligence
- J. Urgen Schmidhuber
- Yoshua Bengio
- Faustino J. Gomez
- Sergey Levine
- Pieter Abbeel
- Quoc V. Le
- Joelle Pineau
- Kyunghyun Cho
In Computer Vision and Pattern Recognition
- J. Urgen Schmidhuber
- Luc Van Gool
- Andrew Zisserman
- Ming-Hsuan Yang
- Xiaogang Wang
- Dacheng Tao
- Trevor Darrell
- Jonathan Masci
In Information Theory
- Joel Veness
- J. Urgen Schmidhuber
- Faustino J. Gomez
- Min Lin
- Marijn F. Stollenga
- Jonathan Masci
- Tor Lattimore
In Signal Processing
- J. Urgen Schmidhuber
- Faustino J. Gomez
- Alex Graves
- Joel Veness
- Chao Du
- Marijn F. Stollenga
- Jonathan Masci
- Yoshua Bengio
Citations in the corpus
- Programmable Agents (2017, neutral): “Use of programs to specify hard deterministic gating is also reminiscent of PAQ8 [24, 18].”
- Deep Networks with Internal Selective Attention through Feedback Connections (2014, methodology): “Iterative processing of images using recurrency has been successfully used for image reconstruction [48], face-localization [49] and compression [50].”
- Online Learning with Gated Linear Networks (2017, background): “Interestingly, while the original motivation for introducing gating was computational (Mahoney, 2000; Knoll and de Freitas, 2012), we show that this technique also adds meaningful representational power as well; • introduce an adaptive regularization technique which allows a Gated Linear Network to…”
- Locality Sensitive Sparse Encoding for Learning World Models Online (2024, background): “Inspired by Knoll & de Freitas (2012), we propose an expressive sparse non-linear feature representation which we call locality sensitive sparse encoding.”