Abstract: Causal inference with spatial, temporal, and meta-analytic data commonly defaults to regression modeling. While widely accepted, such regression approaches can suffer from model ...

A couple of seminal studies published almost 20 years ago found that conservationists needed to start examining whether their actions were actually causing the desired effects. Assessing conservation ...

Abstract: Causal inference and root cause analysis play a crucial role in network performance evaluation and optimization by identifying critical parameters and explaining how the configuration ...

Microsoft has announced the launch of its latest chip, the Maia 200, which the company describes as a silicon workhorse designed for scaling AI inference. The 200, which follows the company’s Maia 100 ...

Today, we’re proud to introduce Maia 200, a breakthrough inference accelerator engineered to dramatically improve the economics of AI token generation. Maia 200 is an AI inference powerhouse: an ...

Baseten, a startup specializing in AI inference, has raised $300 million at a $5 billion valuation, according to people familiar with the matter, more than doubling its valuation.

Abstract: Deep neural networks (DNNs) often struggle with out-of-distribution data, limiting their reliability in real-world visual applications. To address this issue, domain generalization methods ...

In recent years, the big money has flowed toward LLMs and training; but this year, the emphasis is shifting toward AI inference. LAS VEGAS — Not so long ago — last year, let’s say — tech industry ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

ABSTRACT: Determining the causal effect of special education is a critical topic when making educational policy that focuses on student achievement. However, current special education research is ...