activity
20162022
most citedSaliency Learning: Teaching the Model Where to Pay Attention

12 citations · 17 across the 6 of their papers we have counts for

collaborators

17 papers

q-bio.BM2022

Non-equilibrium molecular geometries in graph neural networks

Ali Raza, E. Adrian Henle, Xiaoli Fern

Graph neural networks have become a powerful framework for learning complex structure-property relationships and fast screening of chemical compounds. Recently proposed methods hav…

cs.CL2021

Text Counterfactuals via Latent Optimization and Shapley-Guided Search

Quintin Pope, Xiaoli Z. Fern

We study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model,…

cs.CV20211 cited

The Devils in the Point Clouds: Studying the Robustness of Point Cloud Convolutions

Xingyi Li, Wenxuan Wu, Xiaoli Z. Fern +1

Recently, there has been a significant interest in performing convolution over irregularly sampled point clouds. Since point clouds are very different from regular raster images, i…

cond-mat.mtrl-sci20201 cited

Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks

Ali Raza, Faaiq Waqar, Arni Sturluson +2

Metal-organic framework (MOFs) are nanoporous materials that could be used to capture carbon dioxide from the exhaust gas of fossil fuel power plants to mitigate climate change. In…

cs.IR20203 cited

Relation Extraction with Explanation

Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +1

Recent neural models for relation extraction with distant supervision alleviate the impact of irrelevant sentences in a bag by learning importance weights for the sentences. Effort…

cs.CL2019

Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation

Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +2

We present a new local entity disambiguation system. The key to our system is a novel approach for learning entity representations. In our approach we learn an entity aware extensi…