Search Results for author:
Found 6 papers, 2 papers with code
Differentiable Information Bottleneck for Deterministic Multi-view Clustering
In this work, we propose a new differentiable information bottleneck (DIB) method, which provides a deterministic and analytical MVC solution by fitting the mutual information without the necessity of variational approximation.
Live and Learn: Continual Action Clustering with Incremental Views
Finally, a three-step alternate optimization is proposed, in which the category memory library and consensus partition matrix are optimized.
Crowd Counting With Deep Negative Correlation Learning
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks.
Ranked #9 on
Crowd Counting
on WorldExpo’10
Multi-Task Clustering of Human Actions by Sharing Information
In this work, we present a novel and effective Multi-Task Information Bottleneck (MTIB) clustering method, which is capable of exploring the shared information between multiple action clustering tasks to improve the performance of individual task.
Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization
We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series.
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series
Our work provides an analytical framework with several statistical tools to analyze, evaluate and further improve the symbolic dynamics for knowledge discovery in time series.
