70 citations · 85 across the 7 of their papers we have counts for
7 papers
Federated Learning for Personalized Humor Recognition
Xu Guo, Han Yu, Boyang Li +5
Computational understanding of humor is an important topic under creative language understanding and modeling. It can play a key role in complex human-AI interactions. The challeng…
Multi-Participant Multi-Class Vertical Federated Learning
Siwei Feng, Han Yu
Federated learning (FL) is a privacy-preserving paradigm for training collective machine learning models with locally stored data from multiple participants. Vertical federated lea…
Few-Shot Learning-Based Human Activity Recognition
Siwei Feng, Marco F. Duarte
Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, we propose a few-s…
Autoencoder Based Sample Selection for Self-Taught Learning
Siwei Feng, Han Yu, Marco F. Duarte
Self-taught learning is a technique that uses a large number of unlabeled data as source samples to improve the task performance on target samples. Compared with other transfer lea…
Graph Autoencoder-Based Unsupervised Feature Selection with Broad and Local Data Structure Preservation
Siwei Feng, Marco F. Duarte
Feature selection is a dimensionality reduction technique that selects a subset of representative features from high dimensional data by eliminating irrelevant and redundant featur…
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation
Yuki Itoh, Siwei Feng, Marco F. Duarte +1
This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semi-supervised fashion, assuming the availabi…
Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
Siwei Feng, Yuki Itoh, Mario Parente +1
Hyperspectral signature classification is a quantitative analysis approach for hyperspectral imagery which performs detection and classification of the constituent materials at the…