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The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...

A production-grade pipeline for causal effect estimation on large-scale user interaction data. Implements difference-in-differences, propensity score matching, and automated power analysis on 500K+ ...

Over a decade ago, when I was first starting to pretend I could write about quantum mechanics, I covered a truly bizarre experiment. One half of a pair of entangled photons was sent through a device ...

Companies are spending enormous sums of money on AI systems, and we are now at a point where there are credible alternatives to Nvidia GPUs as the compute engines within these systems. Given the ...

Testimony and evidence in Asif Merchant’s trial has so far portrayed him as a zealous yet bumbling operative who never came close to his mission, which prosecutors say was backed by Iran. By Santul ...

Supervised causal learning has shown promise in causal discovery, yet it often struggles with generalization across diverse interventional settings, particularly when intervention targets are unknown.

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 ...

ABSTRACT: Treatment-Resistant Depression (TRD) remains one of the most challenging subtypes of major depressive disorder, affecting approximately one-third of patients and leading to significant ...