Algorithm
Links to this note
- Adaptive
Computation Time
- Algorithmic
Information theory
- Algorithmic
probability
- ALife 2020
- Alternative
learning mechanisms
- Automated
discovery in complex systems
- Church-Turing
thesis
- Complexity
metrics
- Compression
- Computing in
cellular automata
- Continual
learning
- CPPN
- Dijkstra's
algorithm
- Edge detection
- Elementary
cellular automata
- Fast Marching
method
- Genetic algorithms
- Gradient descent
-
Gradient descent for wide two-layer neural networks – I : Global
convergence
- Graham scan
- Halting
probability
- Hilbert curve
indexing
- Kolmogorov
complexity
- Lempel-Ziv-Welch
algorithm
- Machine learning
- MAP-Elites
- Minimum
description length
- Neural
architecture search
- Neural network
training
- NLP
- Notes
- Notes
on: AI-GAs: AI-generating algorithms, an alternate paradigm for
producing general artificial intelligence by Clune, J.
(2019)
- Notes
on: Diversity preservation in minimal criterion coevolution through
resource limitation by Brant, J. C., & Stanley, K. O.
(2020)
- Notes on:
Evolving Neural Networks through Augmenting Topologies by Stanley,
K. O., & Miikkulainen, R. (2002)
- Notes on:
Intrinsically Motivated Discovery of Diverse Patterns in
Self-Organizing Systems by Reinke, C., Etcheverry, M., & Oudeyer,
P. (2020)
- Notes on:
Modeling systems with internal state using evolino by Wierstra, D.,
Gomez, F. J., & Schmidhuber, J. (2005)
- Notes on: Network
Deconvolution by Ye, C., Evanusa, M., He, H., Mitrokhin, A.,
Goldstein, T., Yorke, J. A., Fermuller, Cornelia, … (2020)
- Notes on:
POET: open-ended coevolution of environments and their optimized
solutions by Wang, R., Lehman, J., Clune, J., & Stanley, K. O.
(2019)
- Quality diversity
- Raven's
progressive matrices
- Schmidhuber on
Consciousness
- Stable marriage
problem
- Supervised
learning
-
Talk: Alife 2020 keynote Luis Zaman - New Frontiers in Alife: What
was old is new again
- Talk:
Alife 2020 keynote Michael Levin - Robot Cancer
- Talk:
Artificial Intelligence: A Guide for Thinking Humans
-
Talk: The Importance of Open-Endedness in AI and Machine
Learning
- The
Elegance of Optimal Transport
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