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
A Probabilistic Fluctuation based Membership Inference Attack for Diffusion Models
Wenjie Fu, Huandong Wang, Chen Gao, Guanghua Liu, Yong Li, Tao Jiang
Compressed Models Decompress Race Biases: What Quantized Models Forget for Fair Face Recognition
Pedro C. Neto, Eduarda Caldeira, Jaime S. Cardoso, Ana F. Sequeira
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics
Zhuohang Li, Chao Yan, Xinmeng Zhang, Gharib Gharibi, Zhijun Yin, Xiaoqian Jiang, Bradley A. Malin
To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills
Carlo Mazzola, Marta Romeo, Francesco Rea, Alessandra Sciutti, Angelo Cangelosi
Fusion-GRU: A Deep Learning Model for Future Bounding Box Prediction of Traffic Agents in Risky Driving Videos
Muhammad Monjurul Karim, Ruwen Qin, Yinhai Wang
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
Seyedarmin Azizi, Mahdi Nazemi, Arash Fayyazi, Massoud Pedram