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- Berglund, Erik, 1993-
(författare)
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Novel Hessian approximations in optimization algorithms
- 2022
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are several benefits of taking the Hessian of the objective function into account when designing optimization algorithms. Compared to using strictly gradient-based algorithms, Hessian-based algorithms usually require fewer iterations to converge. They are generally less sensitive to tuning of parameters and can better handle ill-conditioned problems. Yet, they are not universally used, due to there being several challenges associated with adapting them to various challenging settings. This thesis deals with Hessian-based optimization algorithms for large-scale, distributed and zeroth-order problems. For the large-scale setting, we contribute with a new way of deriving limited memory quasi-Newton methods, which we show can achieve better results than traditional limited memory quasi-Newton methods with less memory for some logistic and linear regression problems. For the distributed setting, we perform an analysis of how the error of a Newton-step is affected by the condition number and the number of iterations of a consensus-algorithm based on averaging, We show that the number of iterations needed to solve a quadratic problem with relative error less than ε grows logarithmically with 1/ε and also with the condition number of the Hessian of the centralized problem. For the zeroth order setting, we exploit the fact that a finite difference estimate of the directional derivative works as an approximate sketching technique, and use this to propose a zeroth order extension of a sketched Newton method that has been developed to solve large-scale problems. With the extension of this method to the zeroth order setting, we address the combined challenge of large-scale and zeroth order problems.
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| 2. |
- Hentati-Sundberg, Jonas, et al.
(författare)
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Seabird surveillance: combining CCTV and artificial intelligence for monitoring and research
- 2023
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Ingår i: Remote Sensing in Ecology and Conservation. - : Wiley. - 2056-3485. ; 9:4, s. 568-581
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Tidskriftsartikel (refereegranskat)abstract
- Ecological research and monitoring need to be able to rapidly convey information that can form the basis of scientifically sound management. Automated sensor systems, especially if combined with artificial intelligence, can contribute to such rapid high-resolution data retrieval. Here, we explore the prospects of automated methods to generate insights for seabirds, which are often monitored for their high conservation value and for being sentinels for marine ecosystem changes. We have developed a system of video surveillance combined with automated image processing, which we apply to common murres Uria aalge. The system uses a deep learning algorithm for object detection (YOLOv5) that has been trained on annotated images of adult birds, chicks and eggs, and outputs time, location, size and confidence level of all detections, frame-by-frame, in the supplied video material. A total of 144 million bird detections were generated from a breeding cliff over three complete breeding seasons (2019–2021). We demonstrate how object detection can be used to accurately monitor breeding phenology and chick growth. Our automated monitoring approach can also identify and quantify rare events that are easily missed in traditional monitoring, such as disturbances from predators. Further, combining automated video analysis with continuous measurements from a temperature logger allows us to study impacts of heat waves on nest attendance in high detail. Our automated system thus produces comparable, and in several cases significantly more detailed, data than those generated from observational field studies. By running in real time on the camera streams, it has the potential to supply researchers and managers with high-resolution up-to-date information on seabird population status. We describe how the system can be modified to fit various types of ecological research and monitoring goals and thereby provide up-to-date support for conservation and ecosystem management.
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