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 Q]: The domain of time-series forecasting has been extensively studied because it is of fundamental importance in many real-life applications. Weather prediction, traffic flow forecasting or sales are compelling examples of sequential phenomena. Predictive models generally make use of the relations between past and future values. However, in the case of stationary time-series, observed values also drastically depend on a number of exogenous features that can be used to improve forecasting quality. In this work, we propose a change of paradigm which consists in learning such features in embeddings vectors within recurrent neural networks. We apply our framework to forecast smart cards tap-in logs in the Parisian subway network. Results show that context-embedded models perform quantitatively better in one-step ahead and multi-step ahead forecasting.
[EX A]: In order to forecast multivariate stationary time-series we learn embeddings containing contextual features within a RNN; we apply the framework on public transportation data

[EX Q]: We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually. Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own. Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent. We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy. While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken. We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both 1) training with only the sparse reward and 2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior. Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.
[EX A]: We propose a formulation of intrinsic motivation that is suitable as an exploration bias in multi-agent sparse-reward synergistic tasks, by encouraging agents to affect the world in ways that would not be achieved if they were acting individually.

[EX Q]: Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data. However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise). In such settings, we hypothesize that self-supervision based solely on the structure of the data at-hand can help. We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e.g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data. We call this approach limited self-supervision, as we limit ourselves to only the data at-hand. We demonstrate the utility of limited self-supervision on three sequence-level classification tasks, two pertaining to real clinical data and one using synthetic data. Within this framework, we introduce novel forms of self-supervision and demonstrate their utility in improving performance on the target task. Our results indicate that limited self-supervision leads to a consistent improvement over a supervised baseline, across a range of domains. In particular, for the task of identifying atrial fibrillation from small amounts of electrocardiogram data, we observe a nearly 13% improvement in the area under the receiver operating characteristics curve (AUC-ROC) relative to the baseline (AUC-ROC=0.55 vs. AUC-ROC=0.62). Limited self-supervision applied to sequential data can aid in learning intermediate representations, making it particularly applicable in settings where data collection is difficult.
[EX A]:
We show that extra unlabeled data is not required for self-supervised auxiliary tasks to be useful for time series classification, and present new and effective auxiliary tasks.