Researchers at Texas Children's Neurological Research Institute (NRI) and Baylor College of Medicine have developed a powerful new tool within the Genome Aggregation Database (gnomAD) to sharpen the ...
Our foray into causal analysis is not yet complete. Until we define the methods of causal inference, we can't get to the deeper insights that causal analysis can provide. This article details many of ...
In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
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Benchmark contamination detection inside AI models: New method survives RL post-training
Benchmark contamination detection method Excess Separability uses AI model activation geometry to detect whether benchmark ...
Diffusion models are widely used in many AI applications, but research on efficient inference-time scalability*, particularly for reasoning and planning (known as System 2 abilities) has been lacking.
The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...
A new study from researchers at Stanford University and Nvidia proposes a way for AI models to keep learning after deployment — without increasing inference costs. For enterprise agents that have to ...
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