ARE NEURAL NETS MODULAR? INSPECTING FUNCTIONAL MODULARITY THROUGH DIFFERENTIABLE

machinelearning::papers::AreNeuralNetworksModular

Introduction

• What is the name of the Modular Neural Nets paper?

  ARE NEURAL NETS MODULAR? INSPECTING FUNCTIONAL MODULARITY THROUGH DIFFERENTIABLE WEIGHT MASKS

  Jürgen Schmidhuber

• What are the main contributions of the modular neural nets paper?

  • binary differentiable weight mask tool for probing model modularity
  • define metrics to measure degree of model modularity P specialize and P reuse

Method

• Modular Neural networks probes submodules using a differentiable weight mask
  • formulate task corresponding to ability that we want to probe
  • train the weight mask on this task while keeping the original model weights frozen
  • masks are binary which only allows for removing individual weights
• Modular Neural Networks trains binarized masks using the Gumbel Softmax estimator
  • weights are initialized to have a high probability of being masked
• Modular Neural Networks analyzes the overlap between two tasks using two metrics
  1. Intersection over Union: degree of overlap of activated weights
  2. Intersection over minimum: number of shared weights over minimum number of weights for one task
• One motivation for having high P specialize in a model is to prevent catastrophic forgetting
  • modifying one submodule should not effect the other modules
• P reused is desirable for a model in order to help with data efficiency and generalization

Results

• Modular Neural Networks conclusion: networks have specialized submodules but tend not to reuse them

• Double Addition task results: show the modules are not reused
  • binary mask for one addition not reused in the other

  

• Theoretical explanations for lack of P_reuse in LSTM language models is that data routing is hard for learned weights to simulate

Conclusions

Overall this paper’s approach and conclusion demonstrate that Neural Networks are not in fact modular. The idea of training a differentiable binary weight mask is simple and interesting. However I wonder if there is a better way to explore modularity of neural networks using a more structured grouping of the model parameters (rather than treating each parameter individually).

Reference

@article{DBLP:journals/corr/abs-2010-02066,
  author    = {R{\'{o}}bert Csord{\'{a}}s and
               Sjoerd van Steenkiste and
               J{\"{u}}rgen Schmidhuber},
  title     = {Are Neural Nets Modular? Inspecting Functional Modularity Through
               Differentiable Weight Masks},
  journal   = {CoRR},
  volume    = {abs/2010.02066},
  year      = {2020},
  url       = {https://arxiv.org/abs/2010.02066},
  eprinttype = {arXiv},
  eprint    = {2010.02066},
  timestamp = {Mon, 12 Oct 2020 17:53:10 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2010-02066.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}