Ensemble Mask Networks
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Abstract (arXiv)
Can an ℝnrightarrow ℝn feedforward network learn matrix-vector multiplication? This study introduces two mechanisms - flexible masking to take matrix inputs, and a unique network pruning to respect the mask's dependency structure. Networks can approximate fixed operations such as matrix-vector multiplication φ(A,x) rightarrow Ax, motivating the mechanisms introduced with applications towards litmus-testing dependencies or interaction order in graph-based models.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 7 references that are in the map. First posted on arXiv 12 Sep 2023.
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7 of 7 references.
References · 7
- 2020What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimationcited by 492
- 2021Training Neural Networks with Fixed Sparse Maskscited by 212
- 2018Neural Arithmetic Logic Unitscited by 69
- 2021An Operator Theoretic View on Pruning Deep Neural Networkscited by 18
- 2019Deep Neural Network Ensembles for Time Series Classificationcited by 13
- 2022Neural Graphical Modelscited by 11
- 2015A Neural Transfer Function for a Smooth and Differentiable Transition Between Additive and Multiplicative Interactionscited by 3
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