Topic models have been prevalent for decades with various applications. However, existing
topic models commonly suffer from the notorious topic collapsing: discovered topics …

Graph Neural Networks (GNNs) have emerged as powerful tools for collaborative filtering. A
key challenge of recommendations is to distill long-range collaborative signals from user …

With the rapid growth of internet based services and the data generated on these services
are attracted by the attackers to intrude the networking services and information. Based on …

Hierarchical topic modeling aims to discover latent topics from a corpus and organize them
into a hierarchy to understand documents with desirable semantic granularity. However …

Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer
in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm …

We propose a new method for unsupervised clustering for collider physics named UCluster,
where information in the embedding space created by a neural network is used to …

Recently, a common starting point for solving complex unsupervised image classification
tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) …

We introduce a differentiable clustering method based on stochastic perturbations of
minimum-weight spanning forests. This allows us to include clustering in end-to-end …

Abstract Federated Semi-supervised Learning (FedSSL) has emerged as a new paradigm
for allowing distributed clients to collaboratively train a machine learning model over scarce …

This paper proposes an integrated approach for a biomethane supply chain from Organic
Fraction of Municipal Solid Waste (OFMSW), addressing both strategic plant location-sizing …