inproceedingsoficlr
Latent Alignment and Variational Attention
Yuntian Deng, Yoon Kim, Justin Chiu, Demi Guo, Alexander Rush
Arelated latent approach, hard attention, fixesthese issues, butisgenerally harder to train and less accurate.
Country:
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > Canada > Quebec > Montreal (0.04)
Technology:
Compacting, Picking and Growing for Unforgetting Continual Learning
Ching-Yi Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, Chu-Song Chen
Continual lifelong learning is essential to many applications.
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- Oceania > Australia > New South Wales > Sydney (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Asia > Taiwan > Taiwan Province > Taipei (0.04)
Technology: Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.47)
Sequence-to-SequenceLearningwith LatentNeuralGrammars
Sequence-to-sequence learning with neural networks has become the de facto standard forsequence prediction tasks.
Country:
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- Asia > Singapore (0.04)
Technology:
Black BoxRipper
In this context, we present a teacher-student framework that can distill the black-box (teacher) model into astudent model with minimal accuracyloss.
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- Europe > Romania (0.05)
- North America > Canada > Ontario > Toronto (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)