256 citations · 325 across the 15 of their papers we have counts for
16 papers
Benchmarking Large Language Models for Math Reasoning Tasks
Kathrin Seßler, Yao Rong, Emek Gözlüklü +1
The use of Large Language Models (LLMs) in mathematical reasoning has become a cornerstone of related research, demonstrating the intelligence of these models and enabling potentia…
P-TA: Using Proximal Policy Optimization to Enhance Tabular Data Augmentation via Large Language Models
Shuo Yang, Chenchen Yuan, Yao Rong +2
A multitude of industries depend on accurate and reasonable tabular data augmentation for their business processes. Contemporary methodologies in generating tabular data revolve ar…
Introduction to Eye Tracking: A Hands-On Tutorial for Students and Practitioners
Enkelejda Kasneci, Hong Gao, Suleyman Ozdel +6
Eye-tracking technology is widely used in various application areas such as psychology, neuroscience, marketing, and human-computer interaction, as it is a valuable tool for unders…
Stepwise Self-Consistent Mathematical Reasoning with Large Language Models
Zilong Zhao, Yao Rong, Dongyang Guo +3
Using Large Language Models for complex mathematical reasoning is difficult, primarily due to the complexity of multi-step reasoning. The main challenges of this process include (1…
I-CEE: Tailoring Explanations of Image Classification Models to User Expertise
Yao Rong, Peizhu Qian, Vaibhav Unhelkar +1
Effectively explaining decisions of black-box machine learning models is critical to responsible deployment of AI systems that rely on them. Recognizing their importance, the field…
Efficient GNN Explanation via Learning Removal-based Attribution
Yao Rong, Guanchu Wang, Qizhang Feng +4
As Graph Neural Networks (GNNs) have been widely used in real-world applications, model explanations are required not only by users but also by legal regulations. However, simultan…
DynStatF: An Efficient Feature Fusion Strategy for LiDAR 3D Object Detection
Yao Rong, Xiangyu Wei, Tianwei Lin +2
Augmenting LiDAR input with multiple previous frames provides richer semantic information and thus boosts performance in 3D object detection, However, crowded point clouds in multi…
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
Yao Rong, Tobias Leemann, Thai-trang Nguyen +6
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of e…
When are Post-hoc Conceptual Explanations Identifiable?
Tobias Leemann, Michael Kirchhof, Yao Rong +2
Interest in understanding and factorizing learned embedding spaces through conceptual explanations is steadily growing. When no human concept labels are available, concept discover…
User Trust on an Explainable AI-based Medical Diagnosis Support System
Yao Rong, Nora Castner, Efe Bozkir +1
Recent research has supported that system explainability improves user trust and willingness to use medical AI for diagnostic support. In this paper, we use chest disease diagnosis…
A Consistent and Efficient Evaluation Strategy for Attribution Methods
Yao Rong, Tobias Leemann, Vadim Borisov +2
With a variety of local feature attribution methods being proposed in recent years, follow-up work suggested several evaluation strategies. To assess the attribution quality across…
Human Attention in Fine-grained Classification
Yao Rong, Wenjia Xu, Zeynep Akata +1
The way humans attend to, process and classify a given image has the potential to vastly benefit the performance of deep learning models. Exploiting where humans are focusing can r…
Artificial Intelligence Methods in In-Cabin Use Cases: A Survey
Yao Rong, Chao Han, Christian Hellert +2
As interest in autonomous driving increases, efforts are being made to meet requirements for the high-level automation of vehicles. In this context, the functionality inside the ve…
Explainable Online Validation of Machine Learning Models for Practical Applications
Wolfgang Fuhl, Yao Rong, Thomas Motz +4
We present a reformulation of the regression and classification, which aims to validate the result of a machine learning algorithm. Our reformulation simplifies the original proble…
Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring
Yao Rong, Zeynep Akata, Enkelejda Kasneci
Numerous car accidents are caused by improper driving maneuvers. Serious injuries are however avoidable if such driving maneuvers are detected beforehand and the driver is assisted…
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
Wolfgang Fuhl, Yao Rong, Enkelejda Kasneci
In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data. We also use these networks for reconstruction, and in conjunction with…