Search Results for author:
Found 10 papers, 4 papers with code
Incomplete Multi-view Clustering via Prototype-based Imputation
Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
Twin Contrastive Learning for Online Clustering
Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
Ranked #1 on
Short Text Clustering
on Biomedical
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e. g.}, gender, race, RNA sequencing technique) from dominating the clustering.
Surgical Phase Recognition in Laparoscopic Cholecystectomy
Automatic recognition of surgical phases in surgical videos is a fundamental task in surgical workflow analysis.
Partially View-aligned Representation Learning with Noise-robust Contrastive Loss
To solve such a less-touched problem without the help of labels, we propose simultaneously learning representation and aligning data using a noise-robust contrastive loss.
Contrastive Learning
Partially View-aligned Multi-view Learning
+1
Contrastive Clustering
In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Ranked #3 on
Image Clustering
on STL-10
(using extra training data)
UVMBench: A Comprehensive Benchmark Suite for Researching Unified Virtual Memory in GPUs
The recent introduction of Unified Virtual Memory (UVM) in GPUs offers a new programming model that allows GPUs and CPUs to share the same virtual memory space, shifts the complex memory management from programmers to GPU driver/ hardware, and enables kernel execution even when memory is oversubscribed.
Hardware Architecture
Many-Objective Estimation of Distribution Optimization Algorithm Based on WGAN-GP
EDA establishes a probability model to describe the distribution of solution from the perspective of population macroscopically by statistical learning method, and then randomly samples the probability model to generate a new population.
Large Scale Many-Objective Optimization Driven by Distributional Adversarial Networks
In this paper, we will propose a novel algorithm based on RVEA[1] framework and using Distributional Adversarial Networks (DAN) [2]to generate new offspring.
Horseshoe Regularization for Machine Learning in Complex and Deep Models
We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
