Comparative Study
Comparative studies are a cornerstone of scientific advancement, rigorously evaluating different approaches to solve a problem or understand a phenomenon. Current research focuses on comparing various machine learning models (e.g., CNNs, Transformers, LLMs, and GANs) across diverse applications, including image classification, natural language processing, and optimization problems. These comparisons often involve analyzing the impact of different hyperparameters, data augmentation techniques, and training strategies on model performance and efficiency, leading to improved algorithms and more effective solutions. The insights gained from these studies are crucial for advancing both theoretical understanding and practical applications across numerous scientific disciplines and industrial sectors.
Papers
A Comparative Study of Transfer Learning for Emotion Recognition using CNN and Modified VGG16 Models
Samay Nathani
A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C
Neil De La Fuente, Daniel A. Vidal Guerra
Domain-Specific Pretraining of Language Models: A Comparative Study in the Medical Field
Tobias Kerner
A Comparative Analysis of Interactive Reinforcement Learning Algorithms in Warehouse Robot Grid Based Environment
Arunabh Bora
Performance Evaluation of Lightweight Open-source Large Language Models in Pediatric Consultations: A Comparative Analysis
Qiuhong Wei, Ying Cui, Mengwei Ding, Yanqin Wang, Lingling Xiang, Zhengxiong Yao, Ceran Chen, Ying Long, Zhezhen Jin, Ximing Xu
Aspect-Based Sentiment Analysis Techniques: A Comparative Study
Dineth Jayakody, Koshila Isuranda, A V A Malkith, Nisansa de Silva, Sachintha Rajith Ponnamperuma, G G N Sandamali, K L K Sudheera
A Comparative Study of DSL Code Generation: Fine-Tuning vs. Optimized Retrieval Augmentation
Nastaran Bassamzadeh, Chhaya Methani
Comparative Analysis of LSTM Neural Networks and Traditional Machine Learning Models for Predicting Diabetes Patient Readmission
Abolfazl Zarghani
Impact of Initialization on Intra-subject Pediatric Brain MR Image Registration: A Comparative Analysis between SyN ANTs and Deep Learning-Based Approaches
Andjela Dimitrijevic, Vincent Noblet, Benjamin De Leener