TriagerX combines dual-transformer content rankings with developer interaction history to improve top-k accuracy for developer and component recommendations in bug triaging across five datasets.
Distilling the knowledge in a neural network
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Continual learning via knowledge distillation achieves SOTA 74.28% accuracy on new compound facial expression classes and 100% in one-shot learning.
citing papers explorer
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TriagerX: Dual Transformers for Bug Triaging Tasks with Content and Interaction Based Rankings
TriagerX combines dual-transformer content rankings with developer interaction history to improve top-k accuracy for developer and component recommendations in bug triaging across five datasets.
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Complex Facial Expression Recognition Using Deep Knowledge Distillation of Basic Features
Continual learning via knowledge distillation achieves SOTA 74.28% accuracy on new compound facial expression classes and 100% in one-shot learning.