mlp
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Amortized Fourier Neural Operators
Fourier Neural Operators (FNOs) have shown promise for solving partial differential equations (PDEs).
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Genre:
- Research Report > Experimental Study (0.93)
- Research Report > New Finding (0.68)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Natural Language (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
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- North America > United States (0.14)
- Asia > Middle East > Jordan (0.04)
- Asia > China (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Genre:
- Research Report > New Finding (0.92)
- Overview (0.92)
Scaling MLPs: AT ale of Inductive Bias
Empirical insights into MLPs are important for multiple reasons.
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- Europe > Switzerland > Zürich > Zürich (0.14)
- North America > United States > Florida > Broward County > Fort Lauderdale (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
Technology:
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
- Information Technology > Artificial Intelligence > Natural Language > Large Language Model (0.68)
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- Europe > France > Auvergne-Rhône-Alpes > Isère > Grenoble (0.04)
- Asia > Middle East > Jordan (0.04)
Genre:
- Research Report > Experimental Study (1.00)
- Research Report > New Finding (0.67)
Technology:
Country:
- North America > United States (0.14)
- Oceania > Australia (0.04)
- North America > Canada > Ontario > Toronto (0.04)
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Technology:
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Software > Programming Languages (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.93)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Logic & Formal Reasoning (0.68)
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- Africa > Middle East > Tunisia > Ben Arous Governorate > Ben Arous (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- North America > United States > Pennsylvania (0.04)
- (2 more...)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.93)
- Information Technology > Artificial Intelligence > Natural Language (0.92)
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Deep neural networks are notorious for being sensitive to small well-chosen perturbations, and estimating the regularity of such architectures is of utmost importance for safe and robust practical applications. In this paper, we investigate one of the key characteristics to assess the regularity of such methods: the Lipschitz constant of deep learning architectures.
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- North America > United States > California > Los Angeles County > Long Beach (0.05)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > Finland > Uusimaa > Helsinki (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.71)