52 citations · 146 across the 24 of their papers we have counts for
26 papers
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…
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,…
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…
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…
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…
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…
Saliency Learning: Teaching the Model Where to Pay Attention
Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi +1
Deep learning has emerged as a compelling solution to many NLP tasks with remarkable performances. However, due to their opacity, such models are hard to interpret and trust. Recen…
Learning Scripts as Hidden Markov Models
J. Walker Orr, Prasad Tadepalli, Janardhan Rao Doppa +2
Scripts have been proposed to model the stereotypical event sequences found in narratives. They can be applied to make a variety of inferences including filling gaps in the narrati…
Attentional Multi-Reading Sarcasm Detection
Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli
Recognizing sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and real world facts. Most of the c…
Event Detection with Neural Networks: A Rigorous Empirical Evaluation
J. Walker Orr, Prasad Tadepalli, Xiaoli Fern
Detecting events and classifying them into predefined types is an important step in knowledge extraction from natural language texts. While the neural network models have generally…
Interpreting Recurrent and Attention-Based Neural Models: a Case Study on Natural Language Inference
Reza Ghaeini, Xiaoli Z. Fern, Prasad Tadepalli
Deep learning models have achieved remarkable success in natural language inference (NLI) tasks. While these models are widely explored, they are hard to interpret and it is often…
Joint Neural Entity Disambiguation with Output Space Search
Hamed Shahbazi, Xiaoli Z. Fern, Reza Ghaeini +3
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Giv…
Dependent Gated Reading for Cloze-Style Question Answering
Reza Ghaeini, Xiaoli Z. Fern, Hamed Shahbazi +1
We present a novel deep learning architecture to address the cloze-style question answering task. Existing approaches employ reading mechanisms that do not fully exploit the interd…
Event Nugget Detection with Forward-Backward Recurrent Neural Networks
Reza Ghaeini, Xiaoli Z. Fern, Liang Huang +1
Traditional event detection methods heavily rely on manually engineered rich features. Recent deep learning approaches alleviate this problem by automatic feature engineering. But…
Weakly-supervised Dictionary Learning
Zeyu You, Raviv Raich, Xiaoli Z. Fern +1
We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have be…
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Reza Ghaeini, Sadid A. Hasan, Vivek Datla +7
We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent enc…
Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data
Behrouz Behmardi, Forrest Briggs, Xiaoli Z. Fern +1
Multi-instance data, in which each object (bag) contains a collection of instances, are widespread in machine learning, computer vision, bioinformatics, signal processing, and soci…
Discriminative Clustering with Relative Constraints
Yuanli Pei, Xiaoli Z. Fern, Rómer Rosales +1
We study the problem of clustering with relative constraints, where each constraint specifies relative similarities among instances. In particular, each constraint $(x_i, x_j, x_k)…
Dynamic Programming for Instance Annotation in Multi-instance Multi-label Learning
Anh T. Pham, Raviv Raich, Xiaoli Z. Fern
Labeling data for classification requires significant human effort. To reduce labeling cost, instead of labeling every instance, a group of instances (bag) is labeled by a single b…
Active Metric Learning from Relative Comparisons
Sicheng Xiong, Rómer Rosales, Yuanli Pei +1
This work focuses on active learning of distance metrics from relative comparison information. A relative comparison specifies, for a data point triplet , that insta…
Novelty Detection Under Multi-Instance Multi-Label Framework
Qi Lou, Raviv Raich, Forrest Briggs +1
Novelty detection plays an important role in machine learning and signal processing. This paper studies novelty detection in a new setting where the data object is represented as a…
Multi-Label Classifier Chains for Bird Sound
Forrest Briggs, Xiaoli Z. Fern, Jed Irvine
Bird sound data collected with unattended microphones for automatic surveys, or mobile devices for citizen science, typically contain multiple simultaneously vocalizing birds of di…
A Lipschitz Exploration-Exploitation Scheme for Bayesian Optimization
Ali Jalali, Javad Azimi, Xiaoli Fern +1
The problem of optimizing unknown costly-to-evaluate functions has been studied for a long time in the context of Bayesian Optimization. Algorithms in this field aim to find the op…
Hybrid Batch Bayesian Optimization
Javad Azimi, Ali Jalali, Xiaoli Fern
Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function eva…