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Found 11 papers, 2 papers with code
Coresets for Archetypal Analysis
Archetypal analysis represents instances as linear mixtures of prototypes (the archetypes) that lie on the boundary of the convex hull of the data.
Joint Optimization of an Autoencoder for Clustering and Embedding
That simple neural network, referred to as the clustering module, can be integrated into a deep autoencoder resulting in a deep clustering model able to jointly learn a clustering and an embedding.
Infinite Mixture Model of Markov Chains
We propose a Bayesian nonparametric mixture model for prediction- and information extraction tasks with an efficient inference scheme.
Toward Supervised Anomaly Detection
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions.
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.
Frame-based Data Factorizations
Archetypal Analysis is the method of choice to compute interpretable matrix factorizations.
Rating Continuous Actions in Spatial Multi-Agent Problems
We study credit assignment problems in spatial multi-agent environments where agents pursue a joint objective.
Principled Interpolation in Normalizing Flows
In this paper, we solve this issue by enforcing a fixed norm and, hence, change the base distribution, to allow for a principled way of interpolation.
Graph Conditional Variational Models: Too Complex for Multiagent Trajectories?
Given a neural network with a graph architecture and/or structured output function, variational autoencoding does not seem to contribute statistically significantly to empirical performance.
