In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
Heuristic search research often deals with finding algorithms for offline planning which aim to minimize the number of expanded nodes or planning time. In online planning, algorithms for real-time search or deadline-aware search have been considered before. However, in this paper, we are interested in the problem of {\em situated temporal planning} in which an agent's plan can depend on exogenous events in the external world, and thus it becomes important to take the passage of time into account during the planning process.  
 Previous work on situated temporal planning has proposed simple pruning strategies, as well as complex schemes for a simplified version of the associated metareasoning problem. 
 In this paper, we propose a simple metareasoning technique,  called the crude greedy scheme, which can be applied in a situated temporal planner. Our empirical evaluation shows that the crude greedy scheme outperforms standard heuristic search based on cost-to-go estimates.

Ex Output:
Metareasoning in a Situated Temporal Planner


Ex Input:
Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels. 
 However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video), 
 utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes. 
 Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces. 
 We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network). 
 We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space. 
 The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.
 Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.

Ex Output:
Conditional VAE on top of latent spaces of pre-trained generative models that enables  transfer between drastically different domains while preserving locality and semantic alignment.


Ex Input:
Recent advances in Neural Variational Inference allowed for a renaissance in latent variable models in a variety of domains involving high-dimensional data. In this paper, we introduce two generic Variational Inference frameworks for generative models of Knowledge Graphs; Latent Fact Model and Latent Information Model.   While traditional variational methods derive an analytical approximation for the intractable distribution over the latent variables, here we construct an inference network conditioned on the symbolic representation of entities and relation types in the Knowledge Graph, to provide the variational distributions. The new framework can create models able to discover underlying probabilistic semantics for the symbolic representation by utilising parameterisable distributions which permit training by back-propagation in the context of neural variational inference, resulting in a highly-scalable method. Under a Bernoulli sampling framework, we provide an alternative justification for commonly used techniques in large-scale stochastic variational inference, which drastically reduces training time at a cost of an additional approximation to the variational lower bound.   The generative frameworks are flexible enough to allow training under any prior distribution that permits a re-parametrisation trick, as well as under any scoring function that permits maximum likelihood estimation of the parameters. Experiment results display the potential and efficiency of this framework by improving upon multiple benchmarks with Gaussian prior representations. Code publicly available on Github.

Ex Output:

Working toward generative knowledge graph models to better estimate predictive uncertainty in knowledge inference.