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:
In most current formulations of adversarial training, the discriminators can be expressed as single-input operators, that is, the mapping they define is separable over observations. In this work, we argue that this property might help explain the infamous mode collapse phenomenon in adversarially-trained generative models. Inspired by discrepancy measures and two-sample tests between probability distributions, we propose distributional adversaries that operate on samples, i.e., on sets of multiple points drawn from a distribution, rather than on single observations. We show how they can be easily implemented on top of existing models. Various experimental results show that generators trained in combination with our distributional adversaries are much more stable and are remarkably less prone to mode collapse than traditional models trained with observation-wise prediction discriminators. In addition, the application of our framework to domain adaptation results in strong improvement over recent state-of-the-art.
answer:
We show that the mode collapse problem in GANs may be explained by a lack of information sharing between observations in a training batch, and propose a distribution-based framework for globally sharing information between gradients that leads to more stable and effective adversarial training.


question:
Extracting relevant information, causally inferring and predicting the future states with high accuracy is a crucial task for modeling complex systems. The endeavor to address these tasks is made even more challenging when we have to deal with high-dimensional heterogeneous data streams. Such data streams often have higher-order inter-dependencies across spatial and temporal dimensions. We propose to perform a soft-clustering of the data and learn its dynamics to produce a compact dynamical model while still ensuring the original objectives of causal inference and accurate predictions. To efficiently and rigorously process the dynamics of soft-clustering, we advocate for an information theory inspired approach that incorporates stochastic calculus and seeks to determine a trade-off between the predictive accuracy and compactness of the mathematical representation. We cast the model construction as a maximization of the compression of the state variables such that the predictive ability and causal interdependence (relatedness) constraints between the original data streams and the compact model are closely bounded. We provide theoretical guarantees concerning the convergence of the proposed learning algorithm. To further test the proposed framework, we consider a high-dimensional Gaussian case study and describe an iterative scheme for updating the new model parameters. Using numerical experiments, we demonstrate the benefits on compression and prediction accuracy for a class of dynamical systems. Finally, we apply the proposed algorithm to the real-world dataset of multimodal sentiment intensity and show improvements in prediction with reduced dimensions.
answer:
Compact perception of dynamical process


question:
 A collection of scientific papers is often accompanied by tags:
  keywords, topics, concepts etc., associated with each paper.
   Sometimes these tags are human-generated, sometimes they are
  machine-generated.   We propose a simple measure of the consistency
  of the tagging of scientific papers: whether these tags are
  predictive for the citation graph links.   Since the authors tend to
  cite papers about the topics close to those of their publications, a
  consistent tagging system could predict citations.   We present an
  algorithm to calculate consistency, and experiments with human- and
  machine-generated tags.   We show that augmentation, i.e. the combination
  of the manual tags with the machine-generated ones, can enhance the
  consistency of the tags.   We further introduce cross-consistency,
  the ability to predict citation links between papers tagged by
  different taggers, e.g. manually and by a machine.
   Cross-consistency can be used to evaluate the tagging quality when
  the amount of labeled data is limited.
answer:

A good tagger gives similar tags to a given paper and the papers it cites