Compositional Pattern Producing Networks : A Novel Abstraction of Development
By Kenneth O. Stanley. 2007
Subfields: Generative Models
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Generative Models
- Jeff Clune
- Sebastian Risi
- Anh Totti Nguyen
- Oriol Vinyals
- Quoc V. Le
- Philip H. S. Torr
- Joel Lehman
- Jason Yosinski
Citations in the corpus
- Perceiver: General Perception with Iterative Attention (2021, methodology): “Here, we use a strategy that has recently gained renewed prominence, both in language and in vision: Fourier feature position encodings (Stanley, 2007; Vaswani et al., 2017; Parmar et al., 2018; Tancik et al., 2020; Mildenhall et al., 2020).”
- Large-Scale Evolution of Image Classifiers (2017, background): “…to: Esteban Real < ereal@google.com > . liest such “neuro-discovery” methods was neuro-evolution (Miller et al., 1989; Stanley & Miikkulainen, 2002; Stanley, 2007; Bayer et al., 2009; Stanley et al., 2009; Breuel & Shafait, 2010; Pugh & Stanley, 2013; Kim & Rigazio, 2015; Zaremba, 2015; Fernando…”
- Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “In the context of genotype-to-phenotype mappings, Compositional Patterns Producing Networks (CPPNs) (Stanley, 2007), and also the previous work of Sims (1991), demonstrated that structured phenotypes can be generated through a function mapping without going through the dynamic developmental process…”
- Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions (2020, background): “A class of neural networks known as compositional pattern-producing networks (CPPNs) (Stanley, 2007) are a candidate for a general encoding mechanism that respects this requirement.”
- With Friends Like These, Who Needs Adversaries? (2018, background): “Such images are easily obtained by both evolutionary algorithms and gradient ascent, under direct encoding of pixel intensities (appearing to consist mostly of noise) and under CPPN [12]-regularised encoding (appearing as abstract mid-level patterns).”