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Found 24 papers, 8 papers with code
Lifted Algorithms for Symmetric Weighted First-Order Model Sampling
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.
AGTGAN: Unpaired Image Translation for Photographic Ancient Character Generation
We evaluate our approach on the photographic ancient character datasets, e. g., OBC306 and CSDD.
On Exact Sampling in the Two-Variable Fragment of First-Order Logic
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?
Complex Handwriting Trajectory Recovery: Evaluation Metrics and Algorithm
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.
Continuous Temporal Graph Networks for Event-Based Graph Data
Hence, we propose Continuous Temporal Graph Networks (CTGNs) to capture the continuous dynamics of temporal graph data.
Controllable Multi-Character Psychology-Oriented Story Generation
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.
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types.
Domain-Liftability of Relational Marginal Polytopes
We study computational aspects of relational marginal polytopes which are statistical relational learning counterparts of marginal polytopes, well-known from probabilistic graphical models.
Improving Neural Relation Extraction with Positive and Unlabeled Learning
We present a novel approach to improve the performance of distant supervision relation extraction with Positive and Unlabeled (PU) Learning.
Knowledge Graph Embedding: A Probabilistic Perspective and Generalization Bounds
We study theoretical properties of embedding methods for knowledge graph completion under the missing completely at random assumption.
McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds
We demonstrate that for many types of dependent data, the forest complexity is small and thus implies good concentration.
Variational Quantum Circuit Model for Knowledge Graphs Embedding
In this work, we propose the first quantum Ans\"atze for the statistical relational learning on knowledge graphs using parametric quantum circuits.
MIDI-VAE: Modeling Dynamics and Instrumentation of Music with Applications to Style Transfer
The interpolations smoothly change pitches, dynamics and instrumentation to create a harmonic bridge between two music pieces.
Symbolic Music Genre Transfer with CycleGAN
In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer.
Quantum generative adversarial network for generating discrete distribution
Quantum machine learning has recently attracted much attention from the community of quantum computing.
Quantum Physics
VC-Dimension Based Generalization Bounds for Relational Learning
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).
PAC-Reasoning in Relational Domains
We consider the problem of predicting plausible missing facts in relational data, given a set of imperfect logical rules.
Natural Language Multitasking: Analyzing and Improving Syntactic Saliency of Hidden Representations
We train multi-task autoencoders on linguistic tasks and analyze the learned hidden sentence representations.
JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
First, a chord LSTM predicts a chord progression based on a chord embedding.
Teaching a Machine to Read Maps with Deep Reinforcement Learning
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.
On the ERM Principle with Networked Data
We consider general weighted ERM and show new universal risk bounds for this problem.
Relational Marginal Problems: Theory and Estimation
In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals.
Learning from networked examples
Many machine learning algorithms are based on the assumption that training examples are drawn independently.
Learning from networked examples in a k-partite graph
Many machine learning algorithms are based on the assumption that training examples are drawn independently.
