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Gliomas are some of the most aggressive types of cancers but the blood–brain barrier acts as an obstacle to therapeutic intervention in brain-related diseases. The blood–brain barrier blocks the permeation of potentially toxic compounds into neural tissue through the interactions of brain endothelial cells with glial cells (astrocytes and pericytes) which(More)
We propose a new approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent latent variable models. Further, our approach allows the ensemble to(More)
Variational approaches are often used to approximate intractable posteriors or nor-malization constants in hierarchical latent variable models. While often effective in practice, it is known that the approximation error can be arbitrarily large. We propose a new class of bounds on the marginal log-likelihood of directed latent variable models. Our approach(More)
An important approach for efficient inference in probabilistic graphical models exploits symmetries among objects in the domain. Symmetric variables (states) are collapsed into meta-variables (meta-states) and inference algorithms are run over the lifted graphical model instead of the flat one. Our paper extends existing definitions of symmetry by(More)
Monte-Carlo Tree Search (MCTS) algorithms such as UCT are an attractive online framework for solving planning under uncertainty problems modeled as a Markov Decision Process. However, MCTS search trees are constructed in flat state and action spaces, which can lead to poor policies for large problems. In a separate research thread, domain abstraction(More)
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