Title: Towards Efficient Posterior Sampling in Deep Neural Networks via Symmetry Removal. Abstract: Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal posterior landscape. Markov Chain Monte Carlo approaches asymptotically recover the true, intractable posterior but are prohibitively expensive for large modern architectures. Local posterior approximations, while often yielding satisfactory results in practice, crudely disregard the posterior geometry. We propose to exploit well-known parameter symmetries induced by neuron interchangeability and output activation to retrieve a drastically reduced -- yet exact -- posterior over uniquely identified parametrizations. To this end, we provide an algorithm for explicit symmetry removal and develop an upper bound on Monte Carlo samples required to capture the reduced posterior. Our experiments suggest that efficient sampling from the functionally relevant part of the posterior is indeed possible, opening up a promising path to faithful uncertainty quantification in deep learning.