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.

Input: Consider Input: Many real-world decision-theoretic planning problems can be naturally modeled with discrete and continuous state Markov decision processes (DC-MDPs). While previous work has addressed automated decision-theoretic planning for DCMDPs, optimal solutions have only been defined so far for limited settings, e.g., DC-MDPs having hyper-rectangular piecewise linear value functions. In this work, we extend symbolic dynamic programming (SDP) techniques to provide optimal solutions for a vastly expanded class of DCMDPs. To address the inherent combinatorial aspects of SDP, we introduce the XADD a continuous variable extension of the algebraic decision diagram (ADD) that maintains compact representations of the exact value function. Empirically, we demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the first optimal automated solutions to DCMDPs with linear and nonlinear piecewise partitioned value functions and showing the advantages of constraint-based pruning for XADDs.

Output: Symbolic Dynamic Programming for Discrete and Continuous State MDPs

Input: Consider Input: In recent years deep neural networks have achieved great success in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most other languages do not enjoy such an abundance of annotated data for sentiment analysis. To combat this problem, we propose the Adversarial Deep Averaging Network (ADAN) to transfer sentiment knowledge learned from labeled English data to lowresource languages where only unlabeled data exists. ADAN is a "Y-shaped" network with two discriminative branches: a sentiment classifier and an adversarial language predictor. Both branches take input from a feature extractor that aims to learn hidden representations that capture the underlying sentiment of the text and are invariant across languages. Experiments on Chinese sentiment classification demonstrate that ADAN significantly outperforms several baselines, including a strong pipeline approach that relies on Google Translate, the state-of-the-art commercial machine translation system.

Output: Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

Input: Consider Input: We study the problem of learning domain invariant representations for time series data while transferring the complex temporal latent dependencies between domains. Our model termed as Variational Recurrent Adversarial Deep Domain Adaptation (VRADA) is built atop a variational recurrent neural network (VRNN) and trains adversarially to capture complex temporal relationships that are domain-invariant. This is (as far as we know) the first to capture and transfer temporal latent dependencies of multivariate time-series data. Through experiments on real-world multivariate healthcare time-series datasets, we empirically demonstrate that learning temporal dependencies helps our model's ability to create domain-invariant representations, allowing our model to outperform current state-of-the-art deep domain adaptation approaches.
Output: VARIATIONAL RECURRENT ADVERSARIAL DEEP DOMAIN ADAPTATION