A new research paper introduces a unified framework for analyzing the representation costs of parametric data-fitting methods. This framework reveals the induced function spaces for various models, including kernel methods, wavelets, and shallow neural networks, as special cases. For deep neural networks with ReLU activations, the paper demonstrates that their native spaces are quasi-Banach spaces where the inductive bias cannot be captured by norms for depths greater than two. AI
IMPACT This research provides a theoretical foundation for understanding the inductive biases of deep neural networks, potentially guiding future model design.
RANK_REASON The cluster contains a research paper published on arXiv detailing new theoretical frameworks for understanding neural networks.
- arXiv
- Besov Spaces
- Deep Neural Networks
- function spaces
- kernel method
- Parametric models for incomplete continuous and categorical longitudinal data
- Quasi-Banach Spaces
- Relu Networks
- Representer Theorems
- Reproducing Kernel Hilbert Spaces
- Shallow Neural Networks for mmWave Radar Based Recognition of Vulnerable Road Users
- Variation Spaces
- wavelets
- alphaXiv
- CatalyzeX Code Finder for Papers
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
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