#Bayesian Deep Learning

Explore functional priors for Bayesian deep learning, focusing on imposing Gaussian process priors on neural networks through Wasserstein distance minimization.

Explore computer vision advancements, from 3D object reconstruction to real-time deep learning systems incorporating geometry and uncertainty, with applications in robotics and mobile technology.

Explore advanced probabilistic inference in hybrid domains using Weighted Model Integration. Learn about solvers, tractability analysis, and applications in Bayesian deep learning.

Explore deep learning limitations and new frontiers, including expressivity, generalization, adversarial attacks, uncertainty, and AutoML. Gain insights into cutting-edge developments in neural networks.

Explore cutting-edge deep learning research, including Bayesian methods, generative modeling, visual concept programming, conformal regression, and heteroskedastic regression analysis.