6 papers
It's LIT! Reliability-Optimized LLMs with Inspectable Tools
Ruixin Zhang, Jon Donnelly, Zhicheng Guo +4
Large language models (LLMs) have exhibited remarkable capabilities across various domains. The ability to call external tools further expands their capability to handle real-world…
"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
Varun Babbar, Zhicheng Guo, Cynthia Rudin
The performance of machine learning models relies heavily on the quality of input data, yet real-world applications often face significant data-related challenges. A common issue a…
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
Jon Donnelly, Zhicheng Guo, Alina Jade Barnett +3
Interpretability is critical for machine learning models in high-stakes settings because it allows users to verify the model's reasoning. In computer vision, prototypical part mode…
Sparse learned kernels for interpretable and efficient medical time series processing
Sully F. Chen, Zhicheng Guo, Cheng Ding +2
Rapid, reliable, and accurate interpretation of medical time-series signals is crucial for high-stakes clinical decision-making. Deep learning methods offered unprecedented perform…
Improving Clinician Performance in Classification of EEG Patterns on the Ictal-Interictal-Injury Continuum using Interpretable Machine Learning
Alina Jade Barnett, Zhicheng Guo, Jin Jing +12
In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored…
SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals
Cheng Ding, Zhicheng Guo, Zhaoliang Chen +3
Foundation models, especially those using transformers as backbones, have gained significant popularity, particularly in language and language-vision tasks. However, large foundati…