arXiv ·
This paper introduces ProgramFC, a fact-checking model that decomposes complex claims into simpler sub-tasks using a library of functions. The model uses LLMs to generate reasoning programs and executes them by delegating sub-tasks, enhancing explainability and data efficiency. Experiments on fact-checking datasets demonstrate ProgramFC's superior performance compared to baseline methods, with publicly available code and data.
arXiv ·
The study compares deep learning models trained via transfer learning from ImageNet (TII-models) against those trained solely on medical images (LMI-models) for disease segmentation. Results show that combining outputs from both model types can improve segmentation performance by up to 10% in certain scenarios. A repository of models, code, and over 10,000 medical images is available on GitHub to facilitate further research.
MBZUAI ·
MBZUAI researchers developed ClinGRAD, a multimodal graph neural network that analyzes genomic data, MRI scans, and clinical information to classify dementia types (Alzheimer's, vascular, etc.). The system addresses the challenge of high misdiagnosis rates (up to 30%) in dementia, where incorrect diagnoses can significantly impact patient life expectancy. ClinGRAD aims to be an interpretable AI system, providing explainability to clinicians. Why it matters: Accurate and early diagnosis of dementia subtypes is crucial for slowing disease progression and improving patient care in the region, where the prevalence of dementia is expected to rise significantly.
MBZUAI ·
MBZUAI hosted a talk on causal AI, featuring Professor Jin Tian from Iowa State University. The talk covered enriching AI systems with causal reasoning capabilities, moving AI beyond prediction to understanding. Professor Tian shared research on causal inference and estimating causal effects from data, using a novel estimator with double/debiased machine learning (DML) properties. Why it matters: Causal AI can improve the explainability, robustness, and adaptability of AI systems, addressing limitations of purely statistical models.
MBZUAI ·
Dr. Xinwei Sun from Microsoft Research Asia presented research on trustworthy AI, focusing on statistical learning with theoretical guarantees. The work covers methods for sparse recovery with false-discovery rate analysis and causal inference tools for robustness and explainability. Consistency and identifiability were addressed theoretically, with applications shown in medical imaging analysis. Why it matters: The research contributes to addressing key limitations of current AI models regarding explainability, reproducibility, robustness, and fairness, which are crucial for real-world applications in sensitive fields like healthcare.
arXiv ·
This research presents a post-hoc framework to explain, verify, and align semantic hierarchies within vision-language model (VLM) encoders like CLIP. The method involves agglomerative clustering of class centroids, naming internal nodes via dictionary matching, and quantifying plausibility against human ontologies. Findings across 13 VLMs and 4 datasets reveal that image encoders are more discriminative, while text encoders induce hierarchies better matching human taxonomies, showing a trade-off between zero-shot accuracy and ontological plausibility. Why it matters: This work provides critical insights into the internal organization of VLM embeddings, which is essential for improving their explainability, reliability, and alignment with human knowledge, leading to more trustworthy AI systems.