Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation
📰 ArXiv cs.AI
Learn how to apply complexity-theoretic rates to understand deep learning's algorithmic foundations and universal approximation capabilities
Action Steps
- Apply complexity-theoretic rates to analyze neural network expressivity
- Analyze the trade-offs between model complexity and training data size
- Use the characterization of universal approximation to design more efficient neural networks
- Evaluate the expressivity of different neural network architectures
- Compare the performance of neural networks with different complexity-theoretic rates
Who Needs to Know This
Researchers and engineers working on deep learning models can benefit from understanding the complexity-theoretic rates and universal approximation capabilities to improve model design and training
Key Insight
💡 Complexity-theoretic rates can be used to analyze and improve the expressivity of neural networks
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🤖 Understand the algorithmic foundations of deep learning with complexity-theoretic rates and universal approximation #DeepLearning #AI
Key Takeaways
Learn how to apply complexity-theoretic rates to understand deep learning's algorithmic foundations and universal approximation capabilities
Full Article
Title: Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation
Abstract:
arXiv:2606.26705v1 Announce Type: cross Abstract: Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily captures complexity through regularity, and therefore does not distinguish intuitively simple and complicated objects with comparable regularity, such as the square-root function and a typical Brownian path. The guiding message is that neural networks should be viewed not only
Abstract:
arXiv:2606.26705v1 Announce Type: cross Abstract: Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily captures complexity through regularity, and therefore does not distinguish intuitively simple and complicated objects with comparable regularity, such as the square-root function and a typical Brownian path. The guiding message is that neural networks should be viewed not only
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