Latest AI and machine learning research in health policy for healthcare professionals.
Reinforcement learning (RL) post-training of Diffusion Transformers (DiTs) is prohibitively expensive, requiring thousands of high-end GPUs. Existing works explore two directions to reduce cost: seed exploration improves training convergence by selecting high-contrast samples, yet adds compute to the critical path; spot GPUs offer 69--77\% lower cost, yet sit idle during training because DiT rollo...
Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control. For Type 2 Diabetes (T2D), current guidelines prescribe fixed time intervals between subsequent primary care visits for all patients, overlooking heterogeneity in clinical trajectories and patient characteristics. This study introduces a Contextual Markov Decision Process (CM...
The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict privacy regulations...
End-to-end autonomous driving has achieved state-of-the-art performance on benchmarks and real-world deployments. Its standard training recipe, howeve...
Background: Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) free-text comments contain actionable feedback, but timely, scal...
Large Language Models (LLMs) are increasingly deployed in healthcare settings, yet their tendency to hallucinate poses risks when clinical decisions a...
Cities deliver basic services through mixed public-private facility networks, including schools, clinics, transit providers, and subsidized service po...
We develop a statistical learning theory for gradient boosting applied to the estimation of covariate-dependent Generalized Pareto (GP) distributions ...
Rubrics have emerged as an alternative to RLVR in open-ended domains where a single ground-truth final answer is not available. Existing rubric-based ...
Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required...
The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision ...
Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmi...
Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for i...
Metal additive manufacturing enables the fabrication of complex parts, but achieving consistent build quality remains challenging due to interactions ...
''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reaso...
High-throughput chromatin accessibility assays such as bulk and single-cell ATAC-seq have generated large collections of processed signal tracks in bi...
Large language models embedded in autonomous agents process trusted instructions and untrusted data in one context window, leaving them open to direct...
Group Relative Policy Optimization (GRPO) has demonstrated remarkable success in aligning text-to-image (T2I) flow models with human preferences. Howe...
Modern diffusion models generate high-quality images and videos, but their iterative denoising process makes inference expensive. Feature caching acce...
Large language models (LLMs) such as ChatGPT are rapidly reshaping healthcare education and simulation-based training in non-technical skills (NTS), y...