Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
Papers
Advancing clinical trial outcomes using deep learning and predictive modelling: bridging precision medicine and patient-centered care
Sydney Anuyah, Mallika K Singh, Hope Nyavor
Toward Non-Invasive Diagnosis of Bankart Lesions with Deep Learning
Sahil Sethi, Sai Reddy, Mansi Sakarvadia, Jordan Serotte, Darlington Nwaudo, Nicholas Maassen, Lewis Shi
Parkinson's Disease Diagnosis Through Deep Learning: A Novel LSTM-Based Approach for Freezing of Gait Detection
Aqib Nazir Mir, Iqra Nissar, Mumtaz Ahmed, Sarfaraz Masood, Danish Raza Rizvi
RUL forecasting for wind turbine predictive maintenance based on deep learning
Syed Shazaib Shah, Tan Daoliang, Sah Chandan Kumar
GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia
Carlo Lucibello, Aurora Rossi
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Mojtaba Lotfaliany, Roohallah Alizadehsanid, Mohammadreza Mohebbi
Knowledge-Based Deep Learning for Time-Efficient Inverse Dynamics
Shuhao Ma, Yu Cao, Ian D. Robertson, Chaoyang Shi, Jindong Liu, Zhi-Qiang Zhang
Effective Rank and the Staircase Phenomenon: New Insights into Neural Network Training Dynamics
Yang Jiang, Yuxiang Zhao, Quanhui Zhu
Automatic Prediction of Stroke Treatment Outcomes: Latest Advances and Perspectives
Zeynel A. Samak, Philip Clatworthy, Majid Mirmehdi
Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals
Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D.Robertson, Zhi-Qiang Zhang
Methodology for Online Estimation of Rheological Parameters in Polymer Melts Using Deep Learning and Microfluidics
Juan Sandubete-López, José L. Risco-Martín, Alexander H. McMillan, Eva Besada-Portas
DeepFEA: Deep Learning for Prediction of Transient Finite Element Analysis Solutions
Georgios Triantafyllou, Panagiotis G. Kalozoumis, George Dimas, Dimitris K. Iakovidis
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning
Amnon Bleich, Antje Linnemann, Bjoern H. Diem, Tim OF Conrad
Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
Yiping Li, Romy van Jaarsveld, Ronald de Jong, Jasper Bongers, Gino Kuiper, Richard van Hillegersberg, Jelle Ruurda, Marcel Breeuwer, Yasmina Al Khalil
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking
Shahran Rahman Alve
Deep Learning Modeling Method for RF Devices Based on Uniform Noise Training Set
Zhaokun Hu, Yindong Xiao, Houjun Wang, Jiayong Yu, Zihang Gao
Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos
Yucheng Zhang, Koichi Emura, Eiji Watanabe