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
Weight Freezing: A Regularization Approach for Fully Connected Layers with an Application in EEG Classification
Zhengqing Miao, Meirong Zhao
JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection
Chika Komiya, Naoto Yanai, Kyosuke Yamashita, Shingo Okamura
A Block-Coordinate Approach of Multi-level Optimization with an Application to Physics-Informed Neural Networks
Serge Gratton, Valentin Mercier, Elisa Riccietti, Philippe L. Toint
Deep-Learning-Aided Alternating Least Squares for Tensor CP Decomposition and Its Application to Massive MIMO Channel Estimation
Xiao Gong, Wei Chen, Bo Ai, Geert Leus
Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain
Vanessa Liao, Syed Shariyar Murtaza, Yifan Nie, Jimmy Lin
LLM-empowered Chatbots for Psychiatrist and Patient Simulation: Application and Evaluation
Siyuan Chen, Mengyue Wu, Kenny Q. Zhu, Kunyao Lan, Zhiling Zhang, Lyuchun Cui