97 citations · 457 across the 50 of their papers we have counts for
11 papers · 1 filter
Searching for Stage-wise Neural Graphs In the Limit
Xin Zhou, Dejing Dou, Boyang Li
Search space is a key consideration for neural architecture search. Recently, Xie et al. (2019) found that randomly generated networks from the same distribution perform similarly,…
An Empirical Study on the Relation between Network Interpretability and Adversarial Robustness
Adam Noack, Isaac Ahern, Dejing Dou +1
Deep neural networks (DNNs) have had many successes, but they suffer from two major issues: (1) a vulnerability to adversarial examples and (2) a tendency to elude human interpreta…
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
Amir Pouran Ben Veyseh, Franck Dernoncourt, Dejing Dou +1
Definition Extraction (DE) is one of the well-known topics in Information Extraction that aims to identify terms and their corresponding definitions in unstructured texts. This tas…
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks
Isaac Ahern, Adam Noack, Luis Guzman-Nateras +3
The problem of explaining deep learning models, and model predictions generally, has attracted intensive interest recently. Many successful approaches forgo global approximations i…
Learning Conceptual-Contextual Embeddings for Medical Text
Xiao Zhang, Dejing Dou, Ji Wu
External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which…
Improving Cross-Domain Performance for Relation Extraction via Dependency Prediction and Information Flow Control
Amir Pouran Ben Veyseh, Thien Huu Nguyen, Dejing Dou
Relation Extraction (RE) is one of the fundamental tasks in Information Extraction and Natural Language Processing. Dependency trees have been shown to be a very useful source of i…