fenchel-y oung loss
Learning with Fitzpatrick Losses
Fenchel-Y oung losses are a family of loss functions, encompassing the squared, logistic and sparsemax losses, among others. They are convex w.r.t. the model
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- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.68)
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Learning with Fitzpatrick Losses
Fenchel-Y oung losses are a family of loss functions, encompassing the squared, logistic and sparsemax losses, among others. They are convex w.r.t. the model
Technology:
Overleaf Example
This allows us to include clustering in end-to-end trainable pipelines, with efficient gradients.
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- Europe > France > Île-de-France > Paris > Paris (0.04)
- South America > Brazil (0.04)
- North America > United States > Georgia > Chatham County > Savannah (0.04)
- North America > United States > California > San Diego County > San Diego (0.04)
Technology:
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Artificial Intelligence > Representation & Reasoning > Optimization (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Clustering (0.68)
Technology:
Country:
- Europe > France > Île-de-France > Paris > Paris (0.05)
- North America > United States > California (0.04)
- North America > Canada (0.04)
- (2 more...)
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Structured Prediction with Projection Oracles
We identify the marginal polytope, the output space's convex hull,
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- Asia > Middle East > Jordan (0.04)
- North America > Canada (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- (2 more...)
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.68)
- Information Technology > Artificial Intelligence > Machine Learning > Supervised Learning (0.52)
- Information Technology > Artificial Intelligence > Machine Learning > Inductive Learning (0.52)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks (0.47)
