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Title: GitHub - TimeDelta/learning-to-learn: Learning to learn: Memory-Guided Evolution of Computation Graphs via Self-Compressing Fitness-Regularized Latent Space

Open Graph Title: GitHub - TimeDelta/learning-to-learn: Learning to learn: Memory-Guided Evolution of Computation Graphs via Self-Compressing Fitness-Regularized Latent Space

X Title: GitHub - TimeDelta/learning-to-learn: Learning to learn: Memory-Guided Evolution of Computation Graphs via Self-Compressing Fitness-Regularized Latent Space

Description: Learning to learn: Memory-Guided Evolution of Computation Graphs via Self-Compressing Fitness-Regularized Latent Space - TimeDelta/learning-to-learn

Open Graph Description: Learning to learn: Memory-Guided Evolution of Computation Graphs via Self-Compressing Fitness-Regularized Latent Space - TimeDelta/learning-to-learn

X Description: Learning to learn: Memory-Guided Evolution of Computation Graphs via Self-Compressing Fitness-Regularized Latent Space - TimeDelta/learning-to-learn

Opengraph URL: https://github.com/TimeDelta/learning-to-learn

X: @github

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On the Relationship Between Variational Inference and Auto-Associative Memoryhttps://arxiv.org/pdf/2210.08013.pdf
Multifactorial Evolutionary Algorithm with Online Transfer Parameter Estimation: MFEA-IIhttps://www.researchgate.net/profile/Abhishek-Gupta-17/publication/331729696_Multifactorial_Evolutionary_Algorithm_With_Online_Transfer_Parameter_Estimation_MFEA-II/links/5c98495892851cf0ae95ec75/Multifactorial-Evolutionary-Algorithm-With-Online-Transfer-Parameter-Estimation-MFEA-II.pdf
STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networkshttps://www.ijcai.org/proceedings/2019/0189.pdf
Sparse Distributed Memory using N-of-M Codeshttps://patch-diff.githubusercontent.com/TimeDelta/learning-to-learn/blob/main/apt.cs.manchester.ac.uk/ftp/pub/apt/papers/NofMnnV3.pdf
A discrete time neural network model with spiking neurons: II: Dynamics with noisehttps://arxiv.org/abs/1709.06206
The Information Bottleneck Problem and Its Applications in Machine Learninghttps://arxiv.org/pdf/2004.14941.pdf
Neurons detect cognitive boundaries to structure episodic memories in humanshttps://authors.library.caltech.edu/107546/4/41593_2022_1020_MOESM1_ESM.pdf
Why think step-by-step? Reasoning emerges from the locality of experiencehttps://arxiv.org/pdf/2304.03843.pdf
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Timehttps://arxiv.org/abs/2112.11231#:~:text=Here%2C%20we%20show%20how%20a,regularized%20risk%20on%20the%20loss.
Dynamics-Aware Unsupervised Discovery of Skillshttps://arxiv.org/abs/1907.01657
Frahmentation Instability in Aggregating Systemshttps://www.sciencedirect.com/science/article/abs/pii/S0378437122000930
Rethinking the performance comparison between SNNs and ANNshttps://www.sciencedirect.com/science/article/abs/pii/S0893608019302667
A survey on computationally efficient neural architecture searchhttps://www.sciencedirect.com/science/article/pii/S2949855422000028
Scaling Laws for Reward Model Overoptimizationhttps://arxiv.org/abs/2210.10760
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