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.

Variational inference (VI) is a popular approach for approximate Bayesian inference that is particularly promising for highly parameterized models such as deep neural networks.   A key challenge of variational inference is to approximate the posterior over model parameters with a distribution that is simpler and tractable yet sufficiently expressive. In this work, we propose a method for training highly flexible variational distributions by starting with a coarse approximation and iteratively refining it. Each refinement step makes cheap, local adjustments and only requires optimization of simple variational families. We demonstrate theoretically that our method always improves a bound on the approximation (the Evidence Lower BOund) and observe this empirically across a variety of benchmark tasks.   In experiments, our method consistently outperforms recent variational inference methods for deep learning in terms of log-likelihood and the ELBO.   We see that the gains are further amplified on larger scale models, significantly outperforming standard VI and deep ensembles on residual networks on CIFAR10.
The paper proposes an algorithm to increase the flexibility of the variational posterior in Bayesian neural networks through iterative optimization.

This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property. LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and liveness. We convert the LTL property into a limit deterministic Buchi automaton with which a synchronized product MDP is constructed. The control policy is then synthesised by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP. The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).
As safety is becoming a critical notion in machine learning we believe that this work can act as a foundation for a number of research directions such as safety-aware learning algorithms.

Multilingual Neural Machine Translation (NMT) systems are capable of translating between multiple source and target languages within a single system. An important indicator of generalization within these systems is the quality of zero-shot translation - translating between language pairs that the system has never seen during training. However, until now, the zero-shot performance of multilingual models has lagged far behind the quality that can be achieved by using a two step translation process that pivots through an intermediate language (usually English). In this work, we diagnose why multilingual models under-perform in zero shot settings. We propose explicit language invariance losses that guide an NMT encoder towards learning language agnostic representations. Our proposed strategies significantly improve zero-shot translation performance on WMT English-French-German and on the IWSLT 2017 shared task, and for the first time, match the performance of pivoting approaches while maintaining performance on supervised directions.

Simple similarity constraints on top of multilingual NMT enables high quality translation between unseen language pairs for the first time.