no code implementations • 2 Oct 2024 • Kenza Amara, Lukas Klein, Carsten Lüth, Paul Jäger, Hendrik Strobelt, Mennatallah El-Assady
Our work investigates how the integration of information from image and text modalities influences the performance and behavior of VLMs in visual question answering (VQA) and reasoning tasks.
no code implementations • 14 May 2024 • Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
The NLP community has begun to take a keen interest in gaining a deeper understanding of text generation, leading to the development of model-agnostic explainable artificial intelligence (xAI) methods tailored to this task.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
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1 code implementation • 14 Feb 2024 • Kenza Amara, Rita Sevastjanova, Mennatallah El-Assady
We adopt a model-based evaluation to compare SyntaxShap and its weighted form to state-of-the-art explainability methods adapted to text generation tasks, using diverse metrics including faithfulness, coherency, and semantic alignment of the explanations to the model.
no code implementations • 5 Feb 2024 • Anna Varbella, Kenza Amara, Blazhe Gjorgiev, Mennatallah El-Assady, Giovanni Sansavini
However, there is a lack of publicly available graph datasets for training and benchmarking ML models in electrical power grid applications.
no code implementations • 9 Nov 2023 • Jialin Chen, Kenza Amara, Junchi Yu, Rex Ying
Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks.
no code implementations • 28 Sep 2023 • Kenza Amara, Mennatallah El-Assady, Rex Ying
Diverse explainability methods of graph neural networks (GNN) have recently been developed to highlight the edges and nodes in the graph that contribute the most to the model predictions.
1 code implementation • 20 Jun 2022 • Kenza Amara, Rex Ying, Zitao Zhang, Zhihao Han, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang
As GNN models are deployed to more mission-critical applications, we are in dire need for a common evaluation protocol of explainability methods of GNNs.
1 code implementation • 26 Jan 2022 • Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu
The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
no code implementations • 17 Dec 2021 • Kenza Amara, Matthijs Douze, Alexandre Sablayrolles, Hervé Jégou
Modern approaches for fast retrieval of similar vectors on billion-scaled datasets rely on compressed-domain approaches such as binary sketches or product quantization.