398 citations · 2.8k across the 98 of their papers we have counts for
7 papers · 1 filter
Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures
Luke Vilnis, Xiang Li, Shikhar Murty +1
Embedding methods which enforce a partial order or lattice structure over the concept space, such as Order Embeddings (OE) (Vendrov et al., 2016), are a natural way to model transi…
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum
Self-paced learning and hard example mining re-weight training instances to improve learning accuracy. This paper presents two improved alternatives based on lightweight estimates…
End-to-End Learning for Structured Prediction Energy Networks
David Belanger, Bishan Yang, Andrew McCallum
Structured Prediction Energy Networks (SPENs) are a simple, yet expressive family of structured prediction models (Belanger and McCallum, 2016). An energy function over candidate s…
Ask the GRU: Multi-Task Learning for Deep Text Recommendations
Trapit Bansal, David Belanger, Andrew McCallum
In a variety of application domains the content to be recommended to users is associated with text. This includes research papers, movies with associated plot summaries, news artic…
Bethe Projections for Non-Local Inference
Luke Vilnis, David Belanger, Daniel Sheldon +1
Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the…
Anytime Belief Propagation Using Sparse Domains
Sameer Singh, Sebastian Riedel, Andrew McCallum
Belief Propagation has been widely used for marginal inference, however it is slow on problems with large-domain variables and high-order factors. Previous work provides useful app…
An Introduction to Conditional Random Fields
Charles Sutton, Andrew McCallum
Often we wish to predict a large number of variables that depend on each other as well as on other observed variables. Structured prediction methods are essentially a combination o…