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Found 8 papers, 0 papers with code
Federated Learning for Personalized Humor Recognition
In this paper, we propose the FedHumor approach for the recognition of humorous content in a personalized manner through Federated Learning (FL).
Multi-Participant Multi-Class Vertical Federated Learning
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants.
Knockoff-Inspired Feature Selection via Generative Models
While variable selection in statistics aims to distinguish between true and false predictors, feature selection in machine learning aims to reduce the dimensionality of the data while preserving the performance of the learning method.
Few-Shot Learning-Based Human Activity Recognition
The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer.
Autoencoder Based Sample Selection for Self-Taught Learning
In this paper, we propose a metric for the relevance between a source sample and the target samples.
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant features.
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availability of a spectral reference library.
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the pixel level in the scene.
