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Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis.

, , , , , and . NeurIPS, page 26005-26014. (2021)

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What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods., , , and . NeurIPS, (2022)Benchmark Analysis of Representative Deep Neural Network Architectures., , , and . CoRR, (2018)Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis., , , , , , and . CVPR, page 16153-16163. IEEE, (2023)RUBi: Reducing Unimodal Biases for Visual Question Answering., , , , and . NeurIPS, page 839-850. (2019)BLOCK: Bilinear Superdiagonal Fusion for Visual Question Answering and Visual Relationship Detection., , , and . AAAI, page 8102-8109. AAAI Press, (2019)MUREL: Multimodal Relational Reasoning for Visual Question Answering., , , and . CVPR, page 1989-1998. Computer Vision Foundation / IEEE, (2019)Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis., , , , , and . NeurIPS, page 26005-26014. (2021)How Good is your Explanation? Algorithmic Stability Measures to Assess the Quality of Explanations for Deep Neural Networks., , , and . WACV, page 1565-1575. IEEE, (2022)Master's Thesis : Deep Learning for Visual Recognition., , and . CoRR, (2016)MUTAN: Multimodal Tucker Fusion for Visual Question Answering., , , and . ICCV, page 2631-2639. IEEE Computer Society, (2017)