Discover how CoreWeave’s bare-metal GPU clusters and new AI workloads could drive massive growth. Find out more about the ...

Explore real-time threat detection in post-quantum AI inference environments. Learn how to protect against evolving threats and secure model context protocol (mcp) deployments with future-proof ...

A reader objects to a defense of disparate-impact theory.

Introduction Armed conflict severely impacts health, with indirect deaths often exceeding direct casualties two to four times ...

Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...

You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...

Statistical models predict stock trends using historical data and mathematical equations. Common statistical models include regression, time series, and risk assessment tools. Effective use depends on ...

Abstract: The debiased estimator is a crucial tool in statistical inference for high-dimensional model parameters. However, constructing such an estimator involves estimating the high-dimensional ...

Thank you again for your great work. I am trying to use a diverse text prompt, but it gives me a meaningful prediction, which is right lung masks from the first example below. image_path = ...

Stochastic gradient descent (SGD) provides a scalable way to compute parameter estimates in applications involving large-scale data or streaming data. As an alternative version, averaged implicit SGD ...

AI inference uses trained data to enable models to make deductions and decisions. Effective AI inference results in quicker and more accurate model responses. Evaluating AI inference focuses on speed, ...