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

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 ...

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: Data-driven soft sensor techniques are increasingly being applied in complex industrial environments, enabling the modeling of many previously intractable variables and playing a critical ...

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 ...

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical ...

In addition to providing a programmatic interface for popular causal inference methods, DoWhy is designed to highlight the critical but often neglected assumptions underlying causal inference analyses ...