TL;DR
It is shown how, given a trained VGAE and an ILP query, a conditional variational auto-encoder can be constructed dynamically that approximates the conditional ELBO without retraining on data.
Stochastic neural network broadens deep learning from prediction‐oriented function approximation to a richer framework for statistical inference, and two related architectures are discussed: kernel‐expanded StoNet and sublinearly structured DNNs, which can be viewed as structural or theoretical extensions of the StoNet...
This work presents a framework for learning continuous latent representations of admissible partial differential equations by embedding a scientific inductive bias directly into the training distribution, and shows that embedding a scientific inductive bias in the training distribution enables the learning of compact a...
Likelihood Score Approximation (LSA), a generative framework that keeps a pretrained unconditional model fixed and learns an observation-conditioned model that approximates the likelihood score from paired samples, is introduced.
In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometrically structured, and sometimes only defined up to a normalizing constant. I start from variational inference and, when analytic update equations are unavailable, move to bl...
A neural regression model (LitEm) that enables transductive knowledge graph embedding models to predict numerical attributes within knowledge graphs and a co-training framework that jointly trains state-of-the-art transductive knowledge graph embedding models with LitEm, which improves link prediction performance mainl...
A boundary-first inference schedule that partitions a model into chunks, first coordinates hidden states at chunk boundaries, and then refines representations within each chunk is proposed, which instantiate in predictive coding networks (PCNs), a local-learning framework in which hidden activities and prediction error...
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