arXiv AI
Jun 16
Deep Neural Networks: A Formulation Via Non-Archimedean Analysis
arXiv:2402. 00094v3 Announce Type: replace-cross Abstract: We introduce a new class of deep neural networks (DNNs) with multilayered tree-like architectures.
EML Trees Are Universal Approximators
The recently introduced EML (Exp-Minus-Log) function acts as continuous analogue of NAND gates, providing a compositional building block capable of representing elementary functions. In this work, we study the expressive power of tree-structured compositions of EML functions.
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Recurrent neural networks approximate continuous functions
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Breaking Chains with Trees: Model-Parallel Deep Learning with $\mathcal{O}(\log N)$ Time Complexity
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arXiv AI
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Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation
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Jul 24
Compiling to recurrent neurons
arXiv:2511. 14953v2 Announce Type: replace-cross Abstract: Discrete structures are currently second-class in differentiable programming.
arXiv Machine Learning
Aug 4
Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions
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arXiv AI
Jun 17
TRACE: Learning to Compute on Circuit Graphs
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arXiv Machine Learning
Jun 16
Floating-Point Networks with Automatic Differentiation Can Represent Almost All Floating-Point Functions and Their Gradients
arXiv:2605. 01702v2 Announce Type: replace Abstract: Theoretical studies show that for any differentiable function on a compact domain, there exists a neural network that approximates both the function values and gradients.
arXiv AI
Jul 13
All you need is SAMPAT
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arXiv Machine Learning
Jun 2
Exploiting weight-space symmetries for approximating curvature
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arXiv AI
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Identifying Good Rules for Efficient SAT Encodings of Single-Constant Multiplication Using Machine Learning
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