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In this work, we propose a novel formulation of planning which views it as a probabilistic inference problem over future optimal …
Recurrent neural networks (RNNs) are powerful architectures to model sequential data, due to their capability to learn short and …
Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily …
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems. In …
Manually authoring transition animations for a complete locomotion system can be a tedious and time-consuming task, especially for …
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model …
The Probably Approximately Correct (PAC) Bayes framework (McAllester, 1999) can incorporate knowledge about the learning algorithm and …
Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process …
Learning long-term dependencies in extended temporal sequences requires credit assignment to events far back in the past. The most …
Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric …