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
Forecasting Response to Treatment with Global Deep Learning and Patient-Specific Pharmacokinetic Priors
Willa Potosnak, Cristian Challu, Kin G. Olivares, Artur Dubrawski
Performance Analysis of UNet and Variants for Medical Image Segmentation
Walid Ehab, Yongmin Li
Understanding Patterns of Deep Learning ModelEvolution in Network Architecture Search
Robert Underwood, Meghana Madhastha, Randal Burns, Bogdan Nicolae