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On the Activation Space of ReLU equipped Deep Neural Networks
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-24412-4_3openalex-snapshot
On the Expressive Power of Deep Architectures
inferredcites →https://doi.org/10.1016/j.neucom.2023.127174openalex-snapshot
Convergence of deep ReLU networks
inferredcites →https://doi.org/10.1137/20m1368902openalex-snapshot
On Transversality of Bent Hyperplane Arrangements and the Topological Expressiveness of ReLU Neural Networks
inferredcites →https://doi.org/10.1137/24m1646996openalex-snapshot
Algorithmic Determination of the Combinatorial Structure of the Linear Regions of ReLU Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1402.1869openalex-snapshot
On the Number of Linear Regions of Deep Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1904.01399openalex-snapshot
On Geometric Structure of Activation Spaces in Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2204.06062openalex-snapshot
Local and global topological complexity measures OF ReLU neural network functions
inferredcites →https://doi.org/10.5860/choice.49-0330openalex-snapshot
Triangulations: structures for algorithms and applications
What points at this work
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inferred← citeshttps://doi.org/10.2139/ssrn.4646606openalex-extracted
Inverse Design of a Nurbs-Based Chiral Metamaterial Via Machine Learning for Programmable Mechanical Deformation
inferred← citeshttps://doi.org/10.1177/14727978251362633openalex-snapshot
Computer-assisted language learning system based on English syntax structure
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