Topic modeling analyzes documents to learn meaningful patterns of words. However,
existing topic models fail to learn interpretable topics when working with large and heavy …

A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs
and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein …

Optimal transport (OT) methods seek a transformation map (or plan) between two probability
measures, such that the transformation has the minimum transportation cost. Such a …

Topic modeling analyzes documents to learn meaningful patterns of words. For documents
collected in sequence, dynamic topic models capture how these patterns vary over time. We …

We propose a topic-guided variational autoencoder (TGVAE) model for text generation.
Distinct from existing variational autoencoder (VAE) based approaches, which assume a …

The past decade has seen an explosion of the amount of digital information generated
within the healthcare domain. Digital data exist in the form of images, video, speech …

As an essential operation of legal retrieval, legal case matching plays a central role in
intelligent legal systems. This task has a high demand on the explainability of matching …

The Large Scale Visual Recognition Challenge based on the well-known Imagenet dataset
catalyzed an intense flurry of progress in computer vision. Benchmark tasks have propelled …

The ability to measure similarity between documents enables intelligent summarization and
analysis of large corpora. Past distances between documents suffer from either an inability …

Mining a set of meaningful and distinctive topics automatically from massive text corpora has
broad applications. Existing topic models, however, typically work in a purely unsupervised …