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
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
HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics
Jingxuan Fan, Sarah Martinson, Erik Y. Wang, Kaylie Hausknecht, Jonah Brenner, Danxian Liu, Nianli Peng, Corey Wang, Michael P. Brenner
Empirical Study of Mutual Reinforcement Effect and Application in Few-shot Text Classification Tasks via Prompt
Chengguang Gan, Tatsunori Mori
Representation-Enhanced Neural Knowledge Integration with Application to Large-Scale Medical Ontology Learning
Suqi Liu, Tianxi Cai, Xiaoou Li
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering
Qihan Qi, Xinsong Yang, Gang Xia, Daniel W. C. Ho, Pengyang Tang
Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging
Sebastian Hirt, Andreas Höhl, Johannes Pohlodek, Joachim Schaeffer, Maik Pfefferkorn, Richard D. Braatz, Rolf Findeisen
Radio Map Prediction from Aerial Images and Application to Coverage Optimization
Fabian Jaensch, Giuseppe Caire, Begüm Demir
Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
Xuanyu Zhuang (LTCI, IP Paris, S2A, IDS), Geoffroy Peeters (LTCI, IP Paris, S2A, IDS), Gaël Richard (S2A, IDS, LTCI, IP Paris)
A Tutorial on the Design, Experimentation and Application of Metaheuristic Algorithms to Real-World Optimization Problems
Eneko Osaba, Esther Villar-Rodriguez, Javier Del Ser, Antonio J. Nebro, Daniel Molina, Antonio LaTorre, Ponnuthurai N.Suganthan, Carlos A. Coello Coello, Francisco Herrera
Edge Computing in Distributed Acoustic Sensing: An Application in Traffic Monitoring
Khanh Truong, Jo Eidsvik, Robin Andre Rørstadbotnen