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Found 8 papers, 6 papers with code
A Practical Approach to Novel Class Discovery in Tabular Data
In particular, the number of novel classes is usually assumed to be known in advance, and their labels are sometimes used to tune hyperparameters.
An Interactive Interface for Novel Class Discovery in Tabular Data
This task is difficult and can often only be performed by a domain expert.
Novel Class Discovery: an Introduction and Key Concepts
We then give an overview of the different families of approaches, organized by the way they transfer knowledge from the labeled set to the unlabeled set.
Découvrir de nouvelles classes dans des données tabulaires
In Novel Class Discovery (NCD), the goal is to find new classes in an unlabeled set given a labeled set of known but different classes.
A Method for Discovering Novel Classes in Tabular Data
In this paper, we propose TabularNCD, a new method for discovering novel classes in tabular data.
Generalized Stochastic Backpropagation
Backpropagating gradients through random variables is at the heart of numerous machine learning applications.
On the Variational Posterior of Dirichlet Process Deep Latent Gaussian Mixture Models
Thanks to the reparameterization trick, deep latent Gaussian models have shown tremendous success recently in learning latent representations.
Large-Scale Characterization and Segmentation of Internet Path Delays with Infinite HMMs
In this article we introduce a new model, the HDP-HMM or infinite hidden Markov model, whose performance in trace segmentation is very close to human cognition.
