activity
20192022
most citedOn the Explainability of Natural Language Processing Deep Models

121 citations · 139 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2022

Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations

Julia El Zini, Mariette Awad

Contrastive explanation methods go beyond transparency and address the contrastive aspect of explanations. Such explanations are emerging as an attractive option to provide actiona…

cs.CL2022121 cited

On the Explainability of Natural Language Processing Deep Models

Julia El Zini, Mariette Awad

While there has been a recent explosion of work on ExplainableAI ExAI on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI c…

cs.CL20228 cited

On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations

Julia El Zini, Mohamad Mansour, Basel Mousi +1

Current Explainable AI (ExAI) methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a comm…

cs.CV20229 cited

BLDNet: A Semi-supervised Change Detection Building Damage Framework using Graph Convolutional Networks and Urban Domain Knowledge

Ali Ismail, Mariette Awad

Change detection is instrumental to localize damage and understand destruction in disaster informatics. While convolutional neural networks are at the core of recent change detecti…

cs.LG2021

Learning to run a Power Network Challenge: a Retrospective Analysis

Antoine Marot, Benjamin Donnot, Gabriel Dulac-Arnold +7

Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand…

cs.CV2020

Generative Adversarial Stacked Autoencoders for Facial Pose Normalization and Emotion Recognition

Ariel Ruiz-Garcia, Vasile Palade, Mark Elshaw +1

In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant…