Results 171 to 180 of about 6,098 (213)

Development and clinical application of a postoperative complication prognosis prediction model for gastric cancer patients based on automated machine learning with body fat rate. [PDF]

Front Oncol
Xue S, Dong X, Wei J, Hao J.
europepmc   +1 more source

Development and Field Validation of WaziSense, a Low-Cost Solar-Powered IoT Smart Tensiometer for Soil-Water Monitoring and Irrigation Scheduling in Semi-Arid Agriculture. [PDF]

Sensors (Basel)
Ben Abdallah H, Naui L, Bakri M, Markwordt F, Rahim MA, Dupont C, Ben Abdallah MA, Rezig M.   +7 more
europepmc   +1 more source

Clinician-Led Code-Free Deep Learning for Detecting Papilledema and Pseudopapilledema Using Optic Disc Imaging. [PDF]

Transl Vis Sci Technol
Shenoy R, Samra GS, Sekhri R, Yoon HJ, Teli S, DeSilva I, Tu Z, Maconachie G, Thomas MG.   +8 more
europepmc   +1 more source

AutoML: A Survey of the State-of-the-Art [PDF]

Knowledge-Based Systems, 2021
automated machine learning (AutoML), published in journal of Knowledge-Based ...
Xin He, Xiaowen Chu, Kaiyong Zhao
exaly   +4 more sources

AutoML From Software Engineering Perspective: Landscapes and Challenges [PDF]

, 2023
Machine learning (ML) has been widely adopted in modern software, but the manual configuration of ML (e.g., hyper-parameter configuration) poses a significant challenge to software developers.
Minghui Zhou, Zhenpeng Chen
exaly   +2 more sources

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AutoML Loss Landscapes

ACM Transactions on Evolutionary Learning and Optimization, 2022
As interest in machine learning and its applications becomes more widespread, how to choose the best models and hyper-parameter settings becomes more important. This problem is known to be challenging for human experts, and consequently, a growing number of methods have been proposed for solving it, giving rise to the area of ...
Yasha Pushak, Holger H. Hoos
openaire   +1 more source

Oracle AutoML

Proceedings of the VLDB Endowment, 2020
Machine learning (ML) is at the forefront of the rising popularity of data-driven software applications. The resulting rapid proliferation of ML technology, explosive data growth, and shortage of data science expertise have caused the industry to face increasingly challenging demands to keep up with fast-paced develop-and-deploy model ...
Anatoly Yakovlev, Hesam Fathi Moghadam, Ali Moharrer, Jingxiao Cai, Nikan Chavoshi, Venkatanathan Varadarajan, Sandeep R. Agrawal, Tomas Karnagel, Sam Idicula, Sanjay Jinturkar, Nipun Agarwal   +10 more
openaire   +2 more sources

Interpretable Feedback for AutoML and a Proposal for Domain-customized AutoML for Networking

Proceedings of the Twentieth ACM Workshop on Hot Topics in Networks, 2021
The barrier to entry for network operators to use machine learning (ML) is high for operators who are not ML experts. Automated machine learning (AutoML) promises operators the ability to train ML models without requiring the expertise of data scientists or the need to learn ML.
Behnaz Arzani, Kevin Hsieh, Haoxian Chen 0001   +2 more
openaire   +1 more source

AutoML

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
Machine learning methods have been adopted for various real-world applications, ranging from social networks, online image/video-sharing platforms, and e-commerce to education, healthcare, etc. However, several components of machine learning methods, including data representation, hyperparameter and model architecture, can largely affect their ...
Yaliang Li, Zhen Wang 0036, Bolin Ding, Ce Zhang 0001   +3 more
openaire   +1 more source