786 citations · 1.5k across the 17 of their papers we have counts for
17 papers
NECA: Network-Embedded Deep Representation Learning for Categorical Data
Xiaonan Gao, Sen Wu, Wenjun Zhou
We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA dee…
Metadata Shaping: Natural Language Annotations for the Tail
Simran Arora, Sen Wu, Enci Liu +1
Language models (LMs) have made remarkable progress, but still struggle to generalize beyond the training data to rare linguistic patterns. Since rare entities and facts are preval…
Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Maya Varma, Laurel Orr, Sen Wu +3
Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare ent…
Bootleg: Chasing the Tail with Self-Supervised Named Entity Disambiguation
Laurel Orr, Megan Leszczynski, Simran Arora +4
A challenge for named entity disambiguation (NED), the task of mapping textual mentions to entities in a knowledge base, is how to disambiguate entities that appear rarely in the t…
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Fan Yang, Hongyang R. Zhang, Sen Wu +2
The problem of learning one task using samples from another task is central to transfer learning. In this paper, we focus on answering the following question: when does combining t…
Train and You'll Miss It: Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings
Mayee F. Chen, Daniel Y. Fu, Frederic Sala +5
Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We…