A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Tzu‐Mao Li, Michaël Gharbi, Andrew Adams, et al.
This paper extends the Halide image processing language with reverse-mode automatic differentiation and automatic scheduling, enabling high-performance gradient computation for arbitrary programs.
David Fournier, Hans J. Skaug, Johnoel Ancheta, et al.
ADMB is a programming framework using automatic differentiation for efficient and accurate statistical inference in highly parameterized nonlinear models.
Krzysztof Czarnecki, Ulrich W. Eisenecker
This book introduces generative programming, a paradigm for automating software component assembly using domain engineering, feature modeling, and code generation.
Kasra Hazeghi, Martin L. Puterman
A unified, rigorous treatment of Markov decision process models covering theoretical, computational, and applied research with a focus on infinite horizon discrete time models.
Unknown
Reinforcement learning boosts LLM performance on competitive programming and software engineering, comparing OpenAI models o1, o1-ioi, and o3.
Unknown
DSPy introduces a programming model that abstracts LM pipelines as text transformation graphs, using declarative modules and automatic optimization to replace manual prompt engineering.
Alison A. Motsinger‐Reif, Scott M. Dudek, Lance W. Hahn, et al.
This paper compares grammatical evolution neural networks (GENN) to genetic programming neural networks for detecting gene-gene interactions in genetic epidemiology, showing GENN outperforms in high-dimensional SNP data.
Rudresh Dwivedi, Devam Dave, Het Naik, et al.
A comprehensive survey of XAI techniques, taxonomies, and programming frameworks to guide stakeholders in selecting appropriate explainability methods.