Overview:  Statistics courses teach practical data analysis skills that can be used in real jobs and business ...

Stop throwing money at GPUs for unoptimized models; using smart shortcuts like fine-tuning and quantization can slash your training costs without losing accuracy.

Abstract: Real-world communicative signals—such as gestures, vocalizations, and facial expressions—are inherently continuous and subtle. Research on emergent communication has been advanced as a ...

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference, previously implemented in MATLAB. VBMC is an approximate inference method ...

Industry groups and drugmakers want the US Food and Drug Administration (FDA) to explicitly clarify that Bayesian statistical methods can be used for products beyond those intended for children and ...

In my day-to-day work, I have spent countless hours optimizing model performance, only to confront a sobering reality: In 2026, the primary barrier to widespread AI adoption has shifted. While raw ...

We introduce a methodology for coding Bayesian statistical models in Python with JAX that follows the design pattern of the Stan probabilistic programming language. This allows a direct, line-by-line ...

Abstract: The paper proposes a new Kalman filtering (KF) algorithm called VBI-MCKF that combines the variational Bayesian inference (VBI)-based KF algorithm and the maximum correntropy KF (MCKF) for ...

According to Andrej Karpathy on X, he released a 243-line, dependency-free Python implementation that can both train and run a GPT model, presenting the full algorithmic content without external ...

ABSTRACT: This study investigates the persistent academic impacts of the Head Start program, a federal government-funded early childhood intervention, using data from the Early Childhood Longitudinal ...