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Found 14 papers, 6 papers with code
Generalizable Face Landmarking Guided by Conditional Face Warping
Given a pair of real and stylized facial images, the conditional face warper predicts a warping field from the real face to the stylized one, in which the face landmarker predicts the ending points of the warping field and provides us with high-quality pseudo landmarks for the corresponding stylized facial images.
Robust Graph Matching Using An Unbalanced Hierarchical Optimal Transport Framework
Given two graphs, we align their node embeddings within the same modality and across different modalities, respectively.
Learning Graphon Autoencoders for Generative Graph Modeling
A linear graphon factorization model works as a decoder, leveraging the latent representations to reconstruct the induced graphons (and the corresponding observed graphs).
Hawkes Processes on Graphons
We propose a novel framework for modeling multiple multivariate point processes, each with heterogeneous event types that share an underlying space and obey the same generative mechanism.
Learning Graphons via Structured Gromov-Wasserstein Barycenters
Accordingly, given a set of graphs generated by an underlying graphon, we learn the corresponding step function as the Gromov-Wasserstein barycenter of the given graphs.
Hierarchical Optimal Transport for Robust Multi-View Learning
Traditional multi-view learning methods often rely on two assumptions: ($i$) the samples in different views are well-aligned, and ($ii$) their representations in latent space obey the same distribution.
Learning Autoencoders with Relational Regularization
A new algorithmic framework is proposed for learning autoencoders of data distributions.
Fused Gromov-Wasserstein Alignment for Hawkes Processes
Accordingly, the learned optimal transport reflects the correspondence between the event types of these two Hawkes processes.
Adversarial Self-Paced Learning for Mixture Models of Hawkes Processes
Instead of learning a mixture model directly from a set of event sequences drawn from different Hawkes processes, the proposed method learns the target model iteratively, which generates "easy" sequences and uses them in an adversarial and self-paced manner.
Interpretable ICD Code Embeddings with Self- and Mutual-Attention Mechanisms
The proposed method achieves clinically-interpretable embeddings of ICD codes, and outperforms state-of-the-art embedding methods in procedure recommendation.
Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching
Using this concept, we extend our method to multi-graph partitioning and matching by learning a Gromov-Wasserstein barycenter graph for multiple observed graphs; the barycenter graph plays the role of the disconnected graph, and since it is learned, so is the clustering.
Gromov-Wasserstein Learning for Graph Matching and Node Embedding
A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes.
Benefits from Superposed Hawkes Processes
The superposition of Hawkes processes is demonstrated to be beneficial for tightening the upper bound of excess risk under certain conditions, and we show the feasibility of the benefit in typical situations.
Learning Hawkes Processes from Short Doubly-Censored Event Sequences
Many real-world applications require robust algorithms to learn point processes based on a type of incomplete data --- the so-called short doubly-censored (SDC) event sequences.
