Application Proficiency
Application proficiency focuses on optimizing the performance and efficiency of algorithms and models across diverse applications, aiming to improve accuracy, speed, and resource utilization. Current research emphasizes developing robust methods for handling model uncertainties and constraints, often employing Bayesian optimization, metaheuristics, and deep learning architectures like convolutional neural networks and transformers. This field is crucial for advancing various domains, from real-time control systems and fraud detection to personalized medicine and environmental monitoring, by enabling the effective deployment of sophisticated computational tools.
900papers
Papers - Page 43
July 4, 2022
A Causal Approach for Business Optimization: Application on an Online Marketplace
Naama Parush, Ohad Levinkron-Fisch, Hanan Shteingart, Amir Bar Sela, Amir Zilberman, Jake KleinMPC with Learned Residual Dynamics with Application on Omnidirectional MAVs
Maximilian Brunner, Weixuan Zhang, Ahmad Roumie, Marco Tognon, Roland Siegwart
June 30, 2022
June 13, 2022
Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africa
Connor T. Jerzak, Fredrik Johansson, Adel DaoudKnowledge Graph Construction and Its Application in Automatic Radiology Report Generation from Radiologist's Dictation
Kaveri Kale, Pushpak Bhattacharyya, Aditya Shetty, Milind Gune, Kush Shrivastava, Rustom Lawyer, Spriha BiswasHigh-Dimensional Bayesian Optimization with Constraints: Application to Powder Weighing
Shoki Miyagawa, Atsuyoshi Yano, Naoko Sawada, Isamu Ogawa
June 12, 2022
June 10, 2022
A new distance measurement and its application in K-Means Algorithm
Yiqun Zhang, Houbiao LiExtending Process Discovery with Model Complexity Optimization and Cyclic States Identification: Application to Healthcare Processes
Liubov O. Elkhovskaya, Alexander D. Kshenin, Marina A. Balakhontceva, Sergey V. KovalchukWeakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Soumick Chatterjee, Hadya Yassin, Florian Dubost, Andreas Nürnberger, Oliver Speck
June 4, 2022
Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui, Gniewosz Leliwa, Michal WroczynskiInitial Study into Application of Feature Density and Linguistically-backed Embedding to Improve Machine Learning-based Cyberbullying Detection
Juuso Eronen, Michal Ptaszynski, Fumito Masui, Gniewosz Leliwa, Michal Wroczynski, Mateusz Piech, Aleksander Smywinski-Pohl
June 3, 2022
Distributional loss for convolutional neural network regression and application to GNSS multi-path estimation
Thomas Gonzalez, Antoine Blais, Nicolas Couëllan, Christian RuizIncremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation
Chenyu You, Jinlin Xiang, Kun Su, Xiaoran Zhang, Siyuan Dong, John Onofrey, Lawrence Staib, James S. Duncan