Deep Learning Model
Deep learning models are complex computational systems designed to learn patterns from data, achieving high accuracy in various tasks like image classification, natural language processing, and time series forecasting. Current research emphasizes improving model efficiency (e.g., through parameter reduction and optimized training algorithms), robustness (e.g., against adversarial attacks and noisy data), and interpretability (e.g., via feature attribution and visualization techniques), often employing architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and transformers. These advancements are driving significant impact across diverse fields, from medical diagnosis and environmental monitoring to industrial automation and personalized medicine.
Papers - Page 5
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
Label-free SERS Discrimination of Proline from Hydroxylated Proline at Single-molecule Level Assisted by a Deep Learning Model
Research Experiment on Multi-Model Comparison for Chinese Text Classification Tasks