A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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The paper proposes a probabilistic framework to characterize LLM agents by their values and understanding, and investigates whether they exhibit social behavior in laboratory experiments.
Armin Steinhauser
TinyCast is an attention-free zero-shot forecaster with 146K parameters that computes periodicity via a spectral detector, achieving state-of-the-art probabilistic accuracy at its size.
Unknown
Proposes Lag-Llama, a univariate probabilistic time-series forecasting model designed for scaling law analyses of time series foundation models.
Unknown
This paper introduces a scalable safe long-horizon planning method that uses copulas to model temporal correlations in uncertainty, providing probabilistic guarantees via conformal prediction.
Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat, et al.
This paper demonstrates that high-quality health knowledge graphs linking diseases and symptoms can be automatically learned from electronic medical records using probabilistic models, with the noisy OR model achieving the best performance.
James B. Grace, Donald R. Schoolmaster, Glenn R. Guntenspergen, et al.
This paper presents guidelines for a third-generation structural equation modeling framework based on graph theory, causal graphs, and probabilistic reasoning.
Y. Linde, A. Buzo, Robert M. Gray
Presents an iterative algorithm for designing vector quantizers from either a probabilistic model or training data, enabling general distortion measures and long blocklengths.
Caroline Pantofaru, Martial Hebert
This paper evaluates mean shift and graph-based segmentation algorithms using the Normalized Probabilistic Rand index for objective comparison.
Luming Zhang, Mingli Song, Qi Zhao, et al.
This paper introduces graphlets (small connected subgraphs) to represent aesthetic features and proposes a probabilistic model for automatic photo cropping via Gibbs sampling.
Frank F. Rosenblatt
This paper introduces the perceptron, a probabilistic model for information storage and organization in the brain, laying the foundation for neural network theory.
Kevin P. Murphy
A comprehensive textbook introducing machine learning through a unified probabilistic perspective, covering foundational theory and modern developments.
Daniel L. Koller, Nir Friedman
This book provides a comprehensive framework for probabilistic graphical models, covering representation, inference, and learning for Bayesian networks, Markov networks, and extensions to dynamical and relational data.