2025
1.
Ozmen Erkin Kokten; Raviv Raich
Maximum Likelihood Estimation of Stable ARX Models using Randomized Coordinate Descent Proceedings Article
In: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5, 2025, (ISSN: 2379-190X).
Abstract | Links | BibTeX | Tags: autoregressive model with exogenous variables, Convergence, coordinate descent, Data models, Finance, Numerical models, Numerical stability, optimization, parameter estimation, Signal processing, Soil Water Content, Speech processing, stability, Stability criteria
@inproceedings{kokten_maximum_2025,
title = {Maximum Likelihood Estimation of Stable ARX Models using Randomized Coordinate Descent},
author = {Ozmen Erkin Kokten and Raviv Raich},
url = {https://ieeexplore.ieee.org/abstract/document/10888613/authors},
doi = {10.1109/ICASSP49660.2025.10888613},
year = {2025},
date = {2025-04-01},
urldate = {2025-04-01},
booktitle = {ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1\textendash5},
abstract = {Autoregressive models play an important role in a variety of applications including finance, engineering, sciences, and agriculture. While for some models (e.g., physics-based models) parameters are known, in other domains the parameters may not be available. This paper deals with the estimation of the parameters of an autoregressive model with exogenous variables. A significant body of literature has explored autoregressive model estimation across different estimation criteria, data availability, and parameterization; however, limited attention has been given to the estimation problem under stability constraints. The incorporation of stability constraints often results in increased computational complexity. As an efficient alternative, we propose to estimate stable ARX parameters using randomized coordinate descent. To demonstrate the efficiency of the proposed approach, we present an empirical convergence study and compare our approach to a state-of-the-art alternative.},
note = {ISSN: 2379-190X},
keywords = {autoregressive model with exogenous variables, Convergence, coordinate descent, Data models, Finance, Numerical models, Numerical stability, optimization, parameter estimation, Signal processing, Soil Water Content, Speech processing, stability, Stability criteria},
pubstate = {published},
tppubtype = {inproceedings}
}
Autoregressive models play an important role in a variety of applications including finance, engineering, sciences, and agriculture. While for some models (e.g., physics-based models) parameters are known, in other domains the parameters may not be available. This paper deals with the estimation of the parameters of an autoregressive model with exogenous variables. A significant body of literature has explored autoregressive model estimation across different estimation criteria, data availability, and parameterization; however, limited attention has been given to the estimation problem under stability constraints. The incorporation of stability constraints often results in increased computational complexity. As an efficient alternative, we propose to estimate stable ARX parameters using randomized coordinate descent. To demonstrate the efficiency of the proposed approach, we present an empirical convergence study and compare our approach to a state-of-the-art alternative.
2.
Ranjan Sapkota; Rizwan Qureshi; Muhammad Usman Hadi; Syed Zohaib Hassan; Ferhat Sadak; Maged Shoman; Muhammad Sajjad; Fayaz Ali Dharejo; Achyut Paudel; Jiajia Li; Zhichao Meng; John Shutske; Manoj Karkee
Multi-Modal LLMs in Agriculture: A Comprehensive Review Journal Article
In: IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 22510–22540, 2025, ISSN: 1558-3783.
Abstract | Links | BibTeX | Tags: Agriculture, Analytical models, ChatGPT, Computational modeling, Computer vision, Data models, Deep learning, Farming, generative artificial intelligence, Hidden Markov models, Large language models (LLMs), Machine Learning, Precision agriculture, Reviews, Training, Transformers, Translation, Vision-language models
@article{sapkota_multi-modal_2025,
title = {Multi-Modal LLMs in Agriculture: A Comprehensive Review},
author = {Ranjan Sapkota and Rizwan Qureshi and Muhammad Usman Hadi and Syed Zohaib Hassan and Ferhat Sadak and Maged Shoman and Muhammad Sajjad and Fayaz Ali Dharejo and Achyut Paudel and Jiajia Li and Zhichao Meng and John Shutske and Manoj Karkee},
url = {https://ieeexplore.ieee.org/document/11173627},
doi = {10.1109/TASE.2025.3612154},
issn = {1558-3783},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IEEE Transactions on Automation Science and Engineering},
volume = {22},
pages = {22510\textendash22540},
abstract = {Given the rapid emergence and applications of Multi-Modal Large Language Models (MM-LLMs) across various scientific fields, insights regarding their applicability in agriculture are still only partially explored. This paper conducts an in-depth review of MM-LLMs in agriculture, focusing on understanding how MM-LLMs can be developed and implemented to optimize agricultural processes, increase efficiency, and reduce costs. Recent studies have explored the capabilities of MM-LLMs in agricultural information processing and decision-making. Despite these advancements, significant gaps persist, particularly in addressing domain-specific challenges such as variable data quality and availability, integration with existing agricultural systems, and the creation of robust training datasets that accurately represent complex agricultural environments. Moreover, a comprehensive understanding of the capabilities, challenges, and limitations of MM-LLMs in agricultural information processing and application is still missing. Exploring these areas is crucial to providing the community with a broader perspective and a clearer understanding of MM-LLMs’ applications, establishing a benchmark for the current state and emerging trends in this field. To bridge this gap, this survey reviews the progress of MM-LLMs and their utilization in agriculture, with an additional focus on 11 key research questions (RQs), where 4 RQs are general and 7 RQs are agriculture focused. By addressing these RQs, this review outlines the current opportunities and challenges, limitations, and future roadmap for MM-LLMs in agriculture. The findings indicate that multi-modal MM-LLMs not only simplify complex agricultural challenges but also significantly enhance decision-making and improve the efficiency of agricultural image processing. These advancements position MM-LLMs as an essential tool for the future of farming. For continued research and understanding, an organized and regularly updated list of papers on MM-LLMs is available at https://github.com/JiajiaLi04/Multi-Modal-LLMs-in-Agriculture Note to Practitioners\textemdashMotivated by the need to optimize agricultural practices, this paper investigates the use of Large Language Models (MM-LLMs) to improve efficiency and decision-making in agriculture. We delve into critical RQs to reveal the capabilities and challenges of MM-LLMs, and their potential applications in the agricultural sector. Looking ahead, our findings suggest a promising future for the integration of MM-LLMs in agriculture, potentially revolutionizing how we manage and operate farms.},
keywords = {Agriculture, Analytical models, ChatGPT, Computational modeling, Computer vision, Data models, Deep learning, Farming, generative artificial intelligence, Hidden Markov models, Large language models (LLMs), Machine Learning, Precision agriculture, Reviews, Training, Transformers, Translation, Vision-language models},
pubstate = {published},
tppubtype = {article}
}
Given the rapid emergence and applications of Multi-Modal Large Language Models (MM-LLMs) across various scientific fields, insights regarding their applicability in agriculture are still only partially explored. This paper conducts an in-depth review of MM-LLMs in agriculture, focusing on understanding how MM-LLMs can be developed and implemented to optimize agricultural processes, increase efficiency, and reduce costs. Recent studies have explored the capabilities of MM-LLMs in agricultural information processing and decision-making. Despite these advancements, significant gaps persist, particularly in addressing domain-specific challenges such as variable data quality and availability, integration with existing agricultural systems, and the creation of robust training datasets that accurately represent complex agricultural environments. Moreover, a comprehensive understanding of the capabilities, challenges, and limitations of MM-LLMs in agricultural information processing and application is still missing. Exploring these areas is crucial to providing the community with a broader perspective and a clearer understanding of MM-LLMs’ applications, establishing a benchmark for the current state and emerging trends in this field. To bridge this gap, this survey reviews the progress of MM-LLMs and their utilization in agriculture, with an additional focus on 11 key research questions (RQs), where 4 RQs are general and 7 RQs are agriculture focused. By addressing these RQs, this review outlines the current opportunities and challenges, limitations, and future roadmap for MM-LLMs in agriculture. The findings indicate that multi-modal MM-LLMs not only simplify complex agricultural challenges but also significantly enhance decision-making and improve the efficiency of agricultural image processing. These advancements position MM-LLMs as an essential tool for the future of farming. For continued research and understanding, an organized and regularly updated list of papers on MM-LLMs is available at https://github.com/JiajiaLi04/Multi-Modal-LLMs-in-Agriculture Note to Practitioners—Motivated by the need to optimize agricultural practices, this paper investigates the use of Large Language Models (MM-LLMs) to improve efficiency and decision-making in agriculture. We delve into critical RQs to reveal the capabilities and challenges of MM-LLMs, and their potential applications in the agricultural sector. Looking ahead, our findings suggest a promising future for the integration of MM-LLMs in agriculture, potentially revolutionizing how we manage and operate farms.
