While the choice of prior is one of the most critical parts of the Bayesian inference workflow,
recent Bayesian deep learning models have often fallen back on vague priors, such as …

Neural networks (NNs) are currently changing the computational paradigm on how to
combine data with mathematical laws in physics and engineering in a profound way …

We explore the link between deep ensembles and Gaussian processes (GPs) through the
lens of the Neural Tangent Kernel (NTK): a recent development in understanding the …

Deep ensembles have recently gained popularity in the deep learning community for their
conceptual simplicity and efficiency. However, maintaining functional diversity between …

Contemporary pose estimation methods enable precise measurements of behavior via
supervised deep learning with hand-labeled video frames. Although effective in many cases …

Deep neural networks have amply demonstrated their prowess but estimating the reliability
of their predictions remains challenging. Deep Ensembles are widely considered as being …

Ensembling neural networks is an effective way to increase accuracy, and can often match
the performance of individual larger models. This observation poses a natural question …

Despite the success of Random Network Distillation (RND) in various domains, it was shown
as not discriminative enough to be used as an uncertainty estimator for penalizing out-of …

Deep reinforcement learning (DRL) and deep multiagent reinforcement learning (MARL)
have achieved significant success across a wide range of domains, including game artificial …

We present a simple and effective approach for posterior uncertainty quantification in deep
operator networks (DeepONets); an emerging paradigm for supervised learning in function …