10 citations · 18 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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,…
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…
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…
Inference on the Change Point for High Dimensional Dynamic Graphical Models
Abhishek Kaul, Hongjin Zhang, Konstantinos Tsampourakis +1
We develop an estimator for the change point parameter for a dynamically evolving graphical model, and also obtain its asymptotic distribution under high dimensional scaling. To pr…
A Graph-Based Approach for Active Learning in Regression
Hongjing Zhang, S. S. Ravi, Ian Davidson
Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine in…
A Framework for Deep Constrained Clustering -- Algorithms and Advances
Hongjing Zhang, Sugato Basu, Ian Davidson
The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as…