multi-label classification
- North America > United States > Texas > Denton County > Denton (0.14)
- North America > United States > New York > Monroe County > Rochester (0.04)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
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A no-regret generalization of hierarchical softmax to extreme multi-label classification
Marek Wydmuch, Kalina Jasinska, Mikhail Kuznetsov, Róbert Busa-Fekete, Krzysztof Dembczynski
Neural Information Processing Systems http://nips.cc/
- Oceania > Australia > New South Wales > Sydney (0.14)
- Europe > Poland > Greater Poland Province > Poznań (0.05)
- North America > Canada > Quebec > Montreal (0.05)
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AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
Ronghui You, Zihan Zhang, Ziye Wang, Suyang Dai, Hiroshi Mamitsuka, Shanfeng Zhu
Neural Information Processing Systems http://nips.cc/
- Asia > China > Shanghai > Shanghai (0.05)
- North America > United States (0.04)
- Asia > Japan > Honshū > Kansai > Kyoto Prefecture > Kyoto (0.04)
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- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
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- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (0.93)
- Asia > China > Guangdong Province > Shenzhen (0.04)
- Asia > Myanmar > Tanintharyi Region > Dawei (0.04)
- Asia > China > Heilongjiang Province > Harbin (0.04)
GAMA: GenerativeAdversarialMulti-Object SceneAttacks
On the other hand, natural scenes include multiple dominant objects that are semantically related.
- North America > United States > California > Riverside County > Riverside (0.04)
- Asia (0.04)
Class-Distribution-Aware Pseudo-Labeling for Semi-Supervised Multi-Label Learning
Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled data.
- North America > United States > Georgia > Fulton County > Atlanta (0.04)
- North America > United States > Maryland > Baltimore County (0.04)
- North America > United States > Maryland > Baltimore (0.04)
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MultilabelClassificationbyHierarchicalPartitioning andData-dependentGrouping
Moreover,inlarge-scale problems, the labels typically have certain (unknown) hierarchy.
- North America > United States > New York > Tompkins County > Ithaca (0.04)
- North America > United States > Massachusetts > Hampshire County > Amherst (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Europe > Switzerland (0.04)
Generalized test utilities for long-tail performance in extreme multi-label classification
Erik Schultheis, Marek Wydmuch, Wojciech Kotłowski, Rohit Babbar, Krzysztof Dembczyński
However, it is often argued that correct predictions in the tail are more "interesting" or "rewarding," but the community has not yet settled on a metric capturing this intuitive concept.
- Europe > Austria > Vienna (0.14)
- Europe > Poland > Greater Poland Province > Poznań (0.04)
- North America > Canada > Quebec > Montreal (0.04)
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