You are all cordially invited to the AMLab seminar on Tuesday February 20 at 16:00 in C3.163, where Bas Veeling will give a talk titled “Uncertainty in Deep Neural Networks with Stochastic Quantized Activation Variational Inference”. Afterwards there are the usual drinks and snacks!
Abstract: The successful uptake of deep neural networks in high-risk domains is contingent on the capability to ensure minimal-risk guarantees. This requires that deep neural networks provide predictive uncertainty of high quality. Amortized variational inference provides a promising direction to achieve this, but demands a flexible yet tractable approximative posterior, which is an open area of research. We propose “SQUAVI”, a novel and flexible variational inference model that imposes a multinomial distribution on quantized latent variables. The proposed method is scalable, self-normalizing and sample efficient, and we demonstrate that the model utilizes the flexible posterior to its full potential, learns interesting non-linearities, and provides predictive uncertainty of competitive quality.