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Found 21 papers, 10 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.
Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model
In this paper, we introduce a novel Distributed Markov Chain Monte Carlo (MCMC) inference method for the Bayesian Non-Parametric Latent Block Model (DisNPLBM), employing the Master/Worker architecture.
Distributed Collapsed Gibbs Sampler for Dirichlet Process Mixture Models in Federated Learning
Dirichlet Process Mixture Models (DPMMs) are widely used to address clustering problems.
Parallel Computation of Multi-Slice Clustering of Third-Order Tensors
Machine Learning approaches like clustering methods deal with massive datasets that present an increasing challenge.
Self-Reinforcement Attention Mechanism For Tabular Learning
Apart from the high accuracy of machine learning models, what interests many researchers in real-life problems (e. g., fraud detection, credit scoring) is to find hidden patterns in data; particularly when dealing with their challenging imbalanced characteristics.
Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression
Genetic algorithms are a well-known example of bio-inspired heuristic methods.
Context Normalization Layer with Applications
Normalization is a pre-processing step that converts the data into a more usable representation.
Multiway clustering of 3-order tensor via affinity matrix
We propose a new method of multiway clustering for 3-order tensors via affinity matrix (MCAM).
DBSCAN of Multi-Slice Clustering for Third-Order Tensors
Several methods for triclustering three-dimensional data require the cluster size or the number of clusters in each dimension to be specified.
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.
Improved Multi-objective Data Stream Clustering with Time and Memory Optimization
The experiments show the ability of our method to partition the data stream in arbitrarily shaped, compact, and well-separated clusters while optimizing the time and memory.
Experience feedback using Representation Learning for Few-Shot Object Detection on Aerial Images
This training strategy encourages the network to adapt to new classes as it would at test time.
Multi-Slice Clustering for 3-order Tensor Data
We analyse, in each dimension or tensor mode, the spectral decomposition of each tensor slice, i. e. a matrix.
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.
Deep Embedded SOM: Joint Representation Learning and Self-Organization
In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map.
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.
