instruction:
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.
question:
Discovering causal structure among a set of variables is a fundamental problem in many empirical sciences. Traditional score-based casual discovery methods rely on various local heuristics to search for a Directed Acyclic Graph (DAG) according to a predefined score function. While these methods, e.g., greedy equivalence search, may have attractive results with infinite samples and certain model assumptions, they are less satisfactory in practice due to finite data and possible violation of assumptions. Motivated by recent advances in neural combinatorial optimization, we propose to use Reinforcement Learning (RL) to search for the DAG with the best scoring. Our encoder-decoder model takes observable data as input and generates graph adjacency matrices that are used to compute rewards. The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity. In contrast with typical RL applications where the goal is to learn a policy, we use RL as a search strategy and our final output would be the graph, among all graphs generated during training, that achieves the best reward. We conduct experiments on both synthetic and real datasets, and show that the proposed approach not only has an improved search ability but also allows for a flexible score function under the acyclicity constraint.
answer:
We apply reinforcement learning to score-based causal discovery and achieve promising results on both synthetic and real datasets


question:
Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem. In particular, how to evaluate a learned generative model is unclear.
 In this paper, we argue that *adversarial learning*, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such as for generating "visually realistic" images. By relating GANs and structured prediction under the framework of statistical decision theory, we put into light links between recent advances in structured prediction theory and the choice of the divergence in GANs. We argue that the insights about the notions of "hard" and "easy" to learn losses can be analogously extended to adversarial divergences. We also discuss the attractive properties of parametric adversarial divergences for generative modeling, and perform experiments to show the importance of choosing a divergence that reflects the final task.
answer:
Parametric adversarial divergences implicitly define more meaningful task losses for generative modeling, we make parallels with structured prediction to study the properties of these divergences and their ability to encode the task of interest.


question:
Deep learning yields great results across many fields,
 from speech recognition, image classification, to translation.
 But for each problem, getting a deep model to work well involves
 research into the architecture and a long period of tuning.

 We present a single model that yields good results on a number
 of problems spanning multiple domains. In particular, this single model
 is trained concurrently on ImageNet, multiple translation tasks,
 image captioning (COCO dataset), a speech recognition corpus,
 and an English parsing task. 

 Our model architecture incorporates building blocks from multiple
 domains. It contains convolutional layers, an attention mechanism,
 and sparsely-gated layers.

 Each of these computational blocks is crucial for a subset of
 the tasks we train on. Interestingly, even if a block is not
 crucial for a task, we observe that adding it never hurts performance
 and in most cases improves it on all tasks.

 We also show that tasks with less data benefit largely from joint
 training with other tasks, while performance on large tasks degrades
 only slightly if at all.
answer:

Large scale multi-task architecture solves ImageNet and translation together and shows transfer learning.