We propose an efficient inference method for switching nonlinear dynamical systems.
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
We propose a new method for semantic instance segmentation, by first computing how likely two pixels are to belong to the same object, and then by grouping similar pixels together.
We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.
Deep convolutional neural networks (DCNNs) trained on a large number of images with strong pixel-level annotations have recently significantly pushed the state-of-art in semantic image segmentation.
We present a new variational inference algorithm for Gaussian processes with non-conjugate likelihood functions.
We show that EM is significantly more robust in the presence of missing data compared to treating the latent factors as parameters, which is the approach used by exponential family PCA and other related matrix-factorization methods.
In this paper we make several contributions towards accelerating approximate Bayesian structural inference for non-decomposable GGMs.