7.4k citations · 22.9k across the 86 of their papers we have counts for
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stat.ML2011★ 5 cited
Loss-sensitive Training of Probabilistic Conditional Random Fields
Maksims N. Volkovs, Hugo Larochelle, Richard S. Zemel
We consider the problem of training probabilistic conditional random fields (CRFs) in the context of a task where performance is measured using a specific loss function. While maxi…
stat.ML2011★ 83 cited
Ranking via Sinkhorn Propagation
Ryan Prescott Adams, Richard S. Zemel
It is of increasing importance to develop learning methods for ranking. In contrast to many learning objectives, however, the ranking problem presents difficulties due to the fact…