Deep Learning
New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Appendix: Improving model calibration with accuracy versus uncertainty optimization A Dataset shift We use various image corruptions and perturbations proposed by Hendrycks and Dietterich
Neural Information Processing Systems
In this section, we follow the same notations described in Section 3.1 of the main paper.
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XCiT: Cross-Covariance Image Transformers
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We propose a "transposed" version of self-attention that operates
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GlanceNets: Interpretable, Leak-proof Concept-based Models
Neural Information Processing Systems
A key requirement is that the concepts be interpretable. Existing CBMs tackle this desideratum using a variety of heuristics based on unclear notions of interpretability, and fail to acquire concepts with the intended semantics.
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