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Found 8 papers, 3 papers with code
Quantifying Local Specialization in Deep Neural Networks
These results suggest that graph-based partitioning can reveal local specialization and that statistical methods can be used to automatedly screen for sets of neurons that can be understood abstractly.
Detecting Modularity in Deep Neural Networks
These results suggest that graph-based partitioning can reveal modularity and help us understand how deep neural networks function.
Clusterability in Neural Networks
We also exhibit novel methods to promote clusterability in neural network training, and find that in multi-layer perceptrons they lead to more clusterable networks with little reduction in accuracy.
Importance and Coherence: Methods for Evaluating Modularity in Neural Networks
We apply these methods on partitionings generated by a spectral clustering algorithm which uses a graph representation of the network's neurons and weights.
Pruned Neural Networks are Surprisingly Modular
To discern structure in these weights, we introduce a measurable notion of modularity for multi-layer perceptrons (MLPs), and investigate the modular structure of MLPs trained on datasets of small images.
Exploring Hierarchy-Aware Inverse Reinforcement Learning
We introduce a new generative model for human planning under the Bayesian Inverse Reinforcement Learning (BIRL) framework which takes into account the fact that humans often plan using hierarchical strategies.
Self-Modification of Policy and Utility Function in Rational Agents
As we continue to create more and more intelligent agents, chances increase that they will learn about this ability.
Loss Bounds and Time Complexity for Speed Priors
On a polynomial time computable sequence our speed prior is computable in exponential time.
