In today's scientific and industrial fields, high-dimensional data in which numerous variables are observed simultaneously, such as genomic, climate, financial, and sensor data, are rapidly increasing ...
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 ...
A Bayesian particle Gibbs framework enables unbiased spike time inference with millisecond resolution and jointly estimates uncertainties in both spike timing and model parameters from fast calcium ...
Morning Overview on MSN
NVIDIA and Microsoft are turning Windows into an agentic AI OS that runs 120-billion-parameter LLMs locally with a 1-million-token context
Researchers have demonstrated that a single consumer-grade GPU with roughly 16 GB of video memory can run million-token ...
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.
This paper describes threats to making valid causal inferences about pandemic impacts on student learning based on cross-year comparisons of average test scores. The paper uses Spring 2021 test score ...
Have researchers discovered a new AI “scaling law”? That’s what some buzz on social media suggests — but experts are skeptical. AI scaling laws, a bit of an informal concept, describe how the ...
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