5 papers
Loud or Silent? A Reusable Framework for Per-Modality Failure Analysis in Multimodal Clinical AI
Quang Bui, Shlok Jaiswal, Samuel Paik-Heintz +14
Multimodal clinical models are usually judged on accuracy with every modality present, but deployment removes modalities; an echocardiogram is often unavailable where an ECG is rou…
Algorithms Trained on Normal Chest X-rays Can Predict Health Insurance Types
Chi-Yu Chen, Rawan Abulibdeh, Arash Asgari +8
Artificial intelligence is revealing what medicine never intended to encode. Deep vision models, trained on chest X-rays, can now detect not only disease but also invisible traces…
Uncertainty Makes It Stable: Curiosity-Driven Quantized Mixture-of-Experts
Sebastián Andrés Cajas Ordóñez, Luis Fernando Torres Torres, Mackenzie J. Meni +6
Deploying deep neural networks on resource-constrained devices faces two critical challenges: maintaining accuracy under aggressive quantization while ensuring predictable inferenc…
Embedding-Aware Quantum-Classical SVMs for Scalable Quantum Machine Learning
Sebastián Andrés Cajas Ordóñez, Luis Fernando Torres Torres, Mario Bifulco +3
Quantum Support Vector Machines face scalability challenges due to high-dimensional quantum states and hardware limitations. We propose an embedding-aware quantum-classical pipelin…
DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
Miguel Aspis, Sebastián A. Cajas Ordónez, Andrés L. Suárez-Cetrulo +1
Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods…