Specifically, we prove the domain-liftability under sampling for the two-variables fragment of first-order logic with counting quantifiers in this paper, by devising an efficient sampling algorithm for this fragment that runs in time polynomial in the domain size.
We evaluate our approach on the photographic ancient character datasets, e. g., OBC306 and CSDD.
In this paper, we study the sampling problem for first-order logic proposed recently by Wang et al. -- how to efficiently sample a model of a given first-order sentence on a finite domain?
Many important tasks such as forensic signature verification, calligraphy synthesis, etc, rely on handwriting trajectory recovery of which, however, even an appropriate evaluation metric is still missing.
In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters.
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphical models.
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.
We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption.
We demonstrate that for many types of dependent data, the forest complexity is small and thus implies good concentration.
In this work, we propose the first quantum Ans\"atze for the statistical relational learning on knowledge graphs using parametric quantum circuits.
The interpolations smoothly change pitches, dynamics and instrumentation to create a harmonic bridge between two music pieces.
In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer.
Quantum machine learning has recently attracted much attention from the community of quantum computing.
In many applications of relational learning, the available data can be seen as a sample from a larger relational structure (e. g. we may be given a small fragment from some social network).
We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations.
Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community.