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Found 10 papers, 1 papers with code
Graph Probability Aggregation Clustering
Traditional clustering methods typically focus on either cluster-wise global clustering or point-wise local clustering to reveal the intrinsic structures in unlabeled data.
Set a Thief to Catch a Thief: Combating Label Noise through Noisy Meta Learning
with the training data as a validation set to evaluate model performance and perform label correction in a meta learning framework, eliminating the need for extra clean data.
Deep Online Probability Aggregation Clustering
Combining machine clustering with deep models has shown remarkable superiority in deep clustering.
Ranked #3 on
Image Clustering
on ImageNet-10
A Multi-module Robust Method for Transient Stability Assessment against False Label Injection Cyberattacks
Leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and progressively enhance robustness and resilience against FLI.
An Interpretable Power System Transient Stability Assessment Method with Expert Guiding Neural-Regression-Tree
TSA-ENRT utilizes an expert guiding nonlinear regression tree to approximate the neural network prediction and the neural network can be explained by the interpretive rules generated by the tree model.
B-LSTM-MIONet: Bayesian LSTM-based Neural Operators for Learning the Response of Complex Dynamical Systems to Length-Variant Multiple Input Functions
This work redesigns MIONet, integrating Long Short Term Memory (LSTM) to learn neural operators from time-dependent data.
Phase Synchrony Component Self-Organization in Brain Computer Interface
Based on this concept, the first deep learning end-to-end network is developed, which directly extracts phase synchrony-based features from raw EEG signals and perform classification.
Self-Evolutionary Clustering
First, most cluster assignment methods are based on simple distance comparison and highly dependent on the target distribution generated by a handcrafted nonlinear mapping.
Fast Dynamic Convolutional Neural Networks for Visual Tracking
RoIPool could lose some positioning precision because it can not handle locations represented by floating numbers.
Clustering Tree-structured Data on Manifold
Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of Euclidean space for appropriate data parameterization and analysis.
