Practical Algorithm
Practical algorithm research focuses on developing and improving algorithms for diverse applications, prioritizing efficiency, accuracy, and interpretability. Current research emphasizes areas like efficient model training and inference (e.g., low-bit quantization for LLMs, distributed algorithms for large datasets), robust optimization techniques (e.g., evolutionary algorithms, Q-learning variants), and methods for handling noisy data or dynamic environments. These advancements have significant implications across various fields, including machine learning, robotics, and data analysis, by enabling more efficient and reliable solutions to complex problems.
Papers
Interpretability methods of machine learning algorithms with applications in breast cancer diagnosis
Panagiota Karatza, Kalliopi V. Dalakleidi, Maria Athanasiou, Konstantina S. Nikita
Structured Prediction Problem Archive
Paul Swoboda, Bjoern Andres, Andrea Hornakova, Florian Bernard, Jannik Irmai, Paul Roetzer, Bogdan Savchynskyy, David Stein, Ahmed Abbas
A multi-domain virtual network embedding algorithm with delay prediction
Peiying Zhang, Xue Pang, Yongjing Ni, Haipeng Yao, Xin Li
Machine Learning and Data Science: Foundations, Concepts, Algorithms, and Tools
Milad Vazan
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning
Peiying Zhang, Chao Wang, Chunxiao Jiang, Abderrahim Benslimane
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Peiying Zhang, Chao Wang, Neeraj Kumar, Weishan Zhang, Lei Liu