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Found 8 papers, 2 papers with code
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models
In this paper we make several contributions towards accelerating approximate Bayesian structural inference for non-decomposable GGMs.
Variational bounds for mixed-data factor analysis
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.
Fast Bayesian Inference for Non-Conjugate Gaussian Process Regression
We present a new variational inference algorithm for Gaussian processes with non-conjugate likelihood functions.
Im2Calories: Towards an Automated Mobile Vision Food Diary
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.
Weakly- and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation
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.
Semantic Instance Segmentation via Deep Metric Learning
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.
Modeling Uncertainty with Hedged Instance Embeddings
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering.
Collapsed Amortized Variational Inference for Switching Nonlinear Dynamical Systems
We propose an efficient inference method for switching nonlinear dynamical systems.
