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 analyzes uncertainty calibration of process reward models, extending evaluation to a 72B parameter variant on MATH500 and AIME24-25 datasets.
De Jong Yeong, Gustavo Velasco-Hernandez, John M. Barry, et al.
This paper provides an end-to-end review of sensor hardware, calibration, and fusion techniques for object detection in autonomous vehicles, highlighting challenges and future research directions.
He-Yen Hsieh, H. T. Kung
ReRound is a post-training quantization method that uses a conditional diffusion model to resolve midpoint ambiguity in round-to-nearest, improving low-bit LLM quantization without calibration data.
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This paper investigates whether large language models can accurately assess their own knowledge and uncertainty, proposing methods to improve self-awareness and calibration.
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This paper investigates confidence calibration in reasoning models, finding that they exhibit superior calibration compared to non-reasoning models.
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.
Zheng Zhang
A flexible camera calibration technique using a planar pattern observed at multiple orientations, with closed-form solution and nonlinear refinement.