1 citations · 1 across the 7 of their papers we have counts for
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LEMON: Local Explanations via Modality-aware OptimizatioN
Yu Qin, Phillip Sloan, Raul Santos-Rodriguez +2
Multimodal models are ubiquitous, yet existing explainability methods are often single-modal, architecture-dependent, or too computationally expensive to run at scale. We introduce…
LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations
Ahmed Khalil, Robert Piechocki, Raul Santos-Rodriguez
In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations. Our method, termed LL-VQ-VAE, replaces the…
Uncertainty Quantification of Surrogate Explanations: an Ordinal Consensus Approach
Jonas Schulz, Rafael Poyiadzi, Raul Santos-Rodriguez
Explainability of black-box machine learning models is crucial, in particular when deployed in critical applications such as medicine or autonomous cars. Existing approaches produc…