activity
20192026
most citedDeep Fair Discriminative Clustering

4 citations · 6 across the 6 of their papers we have counts for

collaborators

9 papers

cs.CV2026

VisRef: Visual Refocusing while Thinking Improves Test-Time Scaling in Multi-Modal Large Reasoning Models

Soumya Suvra Ghosal, Youngeun Kim, Zhuowei Li +6

Advances in large reasoning models have shown strong performance on complex reasoning tasks by scaling test-time compute through extended reasoning. However, recent studies observe…

cs.IR2025

UniSearch: Rethinking Search System with a Unified Generative Architecture

Jiahui Chen, Xiaoze Jiang, Zhibo Wang +18

Modern search systems play a crucial role in facilitating information acquisition. Traditional search engines typically rely on a cascaded architecture, where results are retrieved…

cs.LG20214 cited

Deep Fair Discriminative Clustering

Hongjing Zhang, Ian Davidson

Deep clustering has the potential to learn a strong representation and hence better clustering performance compared to traditional clustering methods such as -means and spectral…

cs.LG2021

Deep Descriptive Clustering

Hongjing Zhang, Ian Davidson

Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images,…

cs.LG20211 cited

A Framework for Deep Constrained Clustering

Hongjing Zhang, Tianyang Zhan, Sugato Basu +1

The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as…

cs.LG20201 cited

Towards Fair Deep Anomaly Detection

Hongjing Zhang, Ian Davidson

Anomaly detection aims to find instances that are considered unusual and is a fundamental problem of data science. Recently, deep anomaly detection methods were shown to achieve su…