Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
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Semialgebraic Optimization for Lipschitz Constants of ReLU Networks
Neural Information Processing Systems
We introduce a semidefinite programming hierarchy to estimate the global and local Lipschitz constant of a multiple layer deep neural network.
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (1.00)
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Long Range Graph Benchmark
Neural Information Processing Systems
Transformer-based methods for graphs that can consider full node connectivity beyond the original sparse structure, thus enabling the modeling of LRI.
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.88)
Revisiting Neural Scaling Laws in Language and Vision
Neural Information Processing Systems
The general approach is to acquire a learning curve, i.e. a
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.69)
Revisiting Neural Scaling Laws in Language and Vision
Neural Information Processing Systems
The general approach is to acquire a learning curve, i.e. a
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- Europe > Switzerland > Zürich > Zürich (0.14)
- Oceania > Australia > Victoria > Melbourne (0.04)
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