A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Rahul Raguram, Ondřej Chum, Marc Pollefeys, et al.
USAC extends RANSAC with a universal framework integrating advanced sampling, verification, and model refinement for robust estimation.
Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, et al.
A comprehensive survey of uncertainty estimation in deep neural networks, covering sources, modeling approaches, calibration, and practical applications.
Michael I. Jordan, Robert A. Jacobs
Introduces a tree-structured supervised learning architecture using hierarchical mixtures of experts with an EM algorithm for parameter estimation.
Ioannis Patras, Ella Hendriks, Reginald L. Lagendijk
This paper proposes a spatio-temporal video segmentation method that labels watershed segments using MAP estimation with a Markov random field model.
Jerome H. Friedman
This paper introduces gradient boosting machines, a general paradigm for function estimation via stagewise additive expansions and steepest-descent minimization in function space.
Hado van Hasselt, Arthur Guez, David Silver
This paper shows that DQN overestimates action values in Atari games and proposes a Double DQN algorithm that reduces overestimation and improves performance.
Unknown
GenPRM scales test-time compute of process reward models via generative reasoning and improved label estimation.