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Found 7 papers, 1 papers with code
DISCO: Internal Evaluation of Density-Based Clustering
In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels.
I Want 'Em All (At Once) -- Ultrametric Cluster Hierarchies
Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen.
SHADE: Deep Density-based Clustering
Similar to existing deep clustering algorithms, SHADE supports high-dimensional and large data sets with the expressive power of a deep autoencoder.
Discovering Individual Rewards in Collective Behavior through Inverse Multi-Agent Reinforcement Learning
In this study, we tackle this challenge by introducing an off-policy inverse multi-agent reinforcement learning algorithm (IMARL).
Deep Clustering With Consensus Representations
Further, we propose DECCS (Deep Embedded Clustering with Consensus representationS), a deep consensus clustering method that learns a consensus representation by enhancing the embedded space to such a degree that all ensemble members agree on a common clustering result.
Remember and Forget Experience Replay for Multi-Agent Reinforcement Learning
We present the extension of the Remember and Forget for Experience Replay (ReF-ER) algorithm to Multi-Agent Reinforcement Learning (MARL).
Learning swimming escape patterns for larval fish under energy constraints
The present, data efficient, reinforcement learning algorithm results in an array of patterns that reveal practical flow optimization principles for efficient swimming and the methodology can be transferred to the control of aquatic robotic devices operating under energy constraints.
