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Found 10 papers, 4 papers with code
Cluster-Based Normalization Layer for Neural Networks
This paper introduces Cluster-Based Normalization (CB-Norm) in two variants - Supervised Cluster-Based Normalization (SCB-Norm) and Unsupervised Cluster-Based Normalization (UCB-Norm) - proposing a groundbreaking one-step normalization approach.
Context-Based Multimodal Fusion
Additionally, the network learns to differentiate embeddings of different modalities through fusion with context and aligns data distributions using a contrastive approach for self-supervised learning.
Context Normalization Layer with Applications
Normalization is a pre-processing step that converts the data into a more usable representation.
Transformer-based conditional generative adversarial network for multivariate time series generation
One of its limitations is that it may generate a random multivariate time series; it may fail to generate samples in the presence of multiple sub-components within an overall distribution.
A Survey and Implementation of Performance Metrics for Self-Organized Maps
Quantitative evaluation of self-organizing maps (SOM) is a subset of clustering validation, which is a challenging problem as such.
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation
The FunCLBM model extends the recently proposed Functional Latent Block Model and allows to create a dependency structure between row and column clusters.
Selecting the Number of Clusters $K$ with a Stability Trade-off: an Internal Validation Criterion
In this perspective, clustering stability has emerged as a natural and model-agnostic principle: an algorithm should find stable structures in the data.
Algorithms for an Efficient Tensor Biclustering
Consider a data set collected by (individuals-features) pairs in different times.
A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering
Mean Shift clustering is a generalization of the k-means clustering which computes arbitrarily shaped clusters as defined as the basins of attraction to the local modes created by the density gradient ascent paths.
Nearest Neighbor Median Shift Clustering for Binary Data
We describe in this paper the theory and practice behind a new modal clustering method for binary data.
