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.
Papers
Explainable Spatio-Temporal GCNNs for Irregular Multivariate Time Series: Architecture and Application to ICU Patient Data
Óscar Escudero-Arnanz, Cristina Soguero-Ruiz, Antonio G. Marques
StepCountJITAI: simulation environment for RL with application to physical activity adaptive intervention
Karine Karine, Benjamin M. Marlin
Uncertainty quantification for fast reconstruction methods using augmented equivariant bootstrap: Application to radio interferometry
Mostafa Cherif, Tobías I. Liaudat, Jonathan Kern, Christophe Kervazo, Jérôme Bobin
Dynamic Matching with Post-allocation Service and its Application to Refugee Resettlement
Kirk Bansak, Soonbong Lee, Vahideh Manshadi, Rad Niazadeh, Elisabeth Paulson
Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application
Keyu Chen, Cheng Fei, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Caitlyn Heqi Yin, Yichao Zhang, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Silin Chen, Weiche Hsieh, Lawrence K.Q. Yan, Chia Xin Liang, Han Xu, Hong-Ming Tseng, Xinyuan Song, Ming Liu
Self-Driving Car Racing: Application of Deep Reinforcement Learning
Florentiana Yuwono, Gan Pang Yen, Jason Christopher
Data Augmentation of Multivariate Sensor Time Series using Autoregressive Models and Application to Failure Prognostics
Douglas Baptista de Souza, Bruno Paes Leao
Development of Minimal Biorobotic Stealth Distance and Its Application in the Design of Direct-Drive Dragonfly-Inspired Aircraft
Zhang Minghao, Song Bifeng, Yang Xiaojun, Wang Liang, Lang Xinyua
SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques
Qiming Guo, Jinwen Tang, Wenbo Sun, Haoteng Tang, Yi Shang, Wenlu Wang
Co-Segmentation without any Pixel-level Supervision with Application to Large-Scale Sketch Classification
Nikolaos-Antonios Ypsilantis, Ondřej Chum