In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and all model parameters have a physical interpretation. This approach results in a transparent and intuitive model. With respect to the state of the art beam model this paper: (i) proposes a different functional form for the probability of range measurements caused by unmodeled objects, (ii) intuitively explains the discontinuity encountered in the state of the art beam model, and (iii) reduces the number of model parameters, while maintaining the same representational power for experimental data. The proposed beam model is called RBBM, short for Rigorously Bayesian Beam Model. A maximum likelihood and a variational Bayesian estimator (both based on expectation-maximization) are proposed to learn the model parameters. Furthermore, the RBBM is extended to a full scan model in two steps: first, to a full scan model for static environments and next, to a full scan model for general, dynamic environments. The full scan model accounts for the dependency between beams and adapts to the local sample density when using a particle filter. In contrast to Gaussian-based state of the art models, the proposed full scan model uses a sample-based approximation. This sample-based approximation enables handling dynamic environments and capturing multimodality, which occurs even in simple static environments.
A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for Range Finders in Dynamic Environments

The EM-algorithm is a general procedure to get maximum likelihood estimates if part of the observations on the variables of a network are missing. In this paper a stochastic version of the algorithm is adapted to probabilistic neural networks describing the associative dependency of variables. These networks have a proba bility distribution, which is a special case of the distribution generated by proba bilistic inference networks. Hence both types of networks can be combined al lowing to integrate probabilistic rules as well as unspecified associations in a sound way. The resulting network may have a number of interesting features including cycles of probabilistic rules, hidden 'un observable' variables, and uncertain and contradictory evidence.
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM Learning Algorithm

Machine transliteration is the process of automatically transforming the script of a word from a source language to a target language, while preserving pronunciation. Sequence to sequence learning has recently emerged as a new paradigm in supervised learning. In this paper a character-based encoder-decoder model has been proposed that consists of two Recurrent Neural Networks. The encoder is a Bidirectional recurrent neural network that encodes a sequence of symbols into a fixed-length vector representation, and the decoder generates the target sequence using an attention-based recurrent neural network. The encoder, the decoder and the attention mechanism are jointly trained to maximize the conditional probability of a target sequence given a source sequence. Our experiments on different datasets show that the proposed encoderdecoder model is able to achieve significantly higher transliteration quality over traditional statistical models.
Neural Machine Transliteration: Preliminary Results