2.9k citations · 12k across the 144 of their papers we have counts for
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Pre-trained Text-to-Image Diffusion Models Are Versatile Representation Learners for Control
Gunshi Gupta, Karmesh Yadav, Yarin Gal +4
Embodied AI agents require a fine-grained understanding of the physical world mediated through visual and language inputs. Such capabilities are difficult to learn solely from task…
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…
DeDUCE: Generating Counterfactual Explanations Efficiently
Benedikt Höltgen, Lisa Schut, Jan M. Brauner +1
When an image classifier outputs a wrong class label, it can be helpful to see what changes in the image would lead to a correct classification. This is the aim of algorithms gener…
Deep Deterministic Uncertainty for Semantic Segmentation
Jishnu Mukhoti, Joost van Amersfoort, Philip H. S. Torr +1
We extend Deep Deterministic Uncertainty (DDU), a method for uncertainty estimation using feature space densities, to semantic segmentation. DDU enables quantifying and disentangli…
Generating Physically-Consistent Satellite Imagery for Climate Visualizations
Björn Lütjens, Brandon Leshchinskiy, Océane Boulais +11
Deep generative vision models are now able to synthesize realistic-looking satellite imagery. But, the possibility of hallucinations prevents their adoption for risk-sensitive appl…
Physics-informed GANs for Coastal Flood Visualization
Björn Lütjens, Brandon Leshchinskiy, Christian Requena-Mesa +8
As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during h…
Evaluating Bayesian Deep Learning Methods for Semantic Segmentation
Jishnu Mukhoti, Yarin Gal
Deep learning has been revolutionary for computer vision and semantic segmentation in particular, with Bayesian Deep Learning (BDL) used to obtain uncertainty maps from deep models…
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
Alex Kendall, Yarin Gal, Roberto Cipolla
Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performa…
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Alex Kendall, Yarin Gal
There are two major types of uncertainty one can model. Aleatoric uncertainty captures noise inherent in the observations. On the other hand, epistemic uncertainty accounts for unc…