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Learn to enhance your LLMs with my 3 step process, inspecting, improving and iterating on your LLMs The post How to Analyze and Optimize Your LLMs in 3 Steps appeared first on Towards Data Science.

A two-year investigation by the International Confederation of Music Publishers (ICMP) and a separate analysis by The Atlantic point to a systematic double standard: Tech giants train AI on copyrighted works scraped at scale, while their own terms of service…

We’ve found the best travel cameras—from point-and-shoot to full-frame—to help you bring home the perfect vacation photos.

Alongside new iPhones, Apple released a new security architecture on Tuesday: Memory Integrity Enforcement aims to eliminate the most frequently exploited class of iOS bugs.

YouTube is rolling out multilingual audio tracks to millions of creators. The article YouTube rolls out multilingual audio tracks to millions of creators appeared first on THE DECODER.

The new pizza oven from All-Clad offers a rotating pizza stone for easy, even bakes. Turns out, I wanted this.

Lightweight, powerful, and generally inexpensive, the handheld vacuum is the perfect household helper.

arXiv:2509.08366v1 Announce Type: new Abstract: We study a missing-value imputation method, termed kNNSampler, that imputes a given unit's missing response by randomly sampling from the observed responses of the $k$ most similar units to the given unit in terms of the observed covariates. This method can sample unknown missing values from their distributions, quantify the uncertainties of missing values, and be readily used for multiple imputation. Unlike popular kNNImputer, which estimates the conditional mean of a missing response given an observed covariate, kNNSampler is theoretically shown to estimate the conditional distribution of a missing response given an observed covariate. Experiments demonstrate its effectiveness in recovering the distribution of missing values. The code for kNNSampler is made publicly available (https://github.com/SAP/knn-sampler).

arXiv:2509.08765v1 Announce Type: cross Abstract: Data-driven acceleration of scientific computing workflows has been a high-profile aim of machine learning (ML) for science, with numerical simulation of transient partial differential equations (PDEs) being one of the main applications. The focus thus far has been on methods that require classical simulations to train, which when combined with the data-hungriness and optimization challenges of neural networks has caused difficulties in demonstrating a convincing advantage against strong classical baselines. We consider an alternative paradigm in which the learner uses a classical solver's own data to accelerate it, enabling a one-shot speedup of the simulation. Concretely, since transient PDEs often require solving a sequence of related linear systems, the feedback from repeated calls to a linear solver such as preconditioned conjugate gradient (PCG) can be used by a bandit algorithm to online-learn an adaptive sequence of solver configurations (e.g. preconditioners). The method we develop, PCGBandit, is implemented directly on top of the popular open source software OpenFOAM, which we use to show its effectiveness on a set of fluid and magnetohydrodynamics (MHD) problems.