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
A Deep Dive into the Connections Between the Renormalization Group and Deep Learning in the Ising Model
Kelsie Taylor
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
Autonomous Detection of Methane Emissions in Multispectral Satellite Data Using Deep Learning
Bertrand Rouet-Leduc, Thomas Kerdreux, Alexandre Tuel, Claudia Hulbert
We Don't Need No Adam, All We Need Is EVE: On The Variance of Dual Learning Rate And Beyond
Afshin Khadangi
What's Race Got to do with it? Predicting Youth Depression Across Racial Groups Using Machine and Deep Learning
Nathan Zhong, Nikhil Yadav
Visual Crowd Analysis: Open Research Problems
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Deep Learning of Delay-Compensated Backstepping for Reaction-Diffusion PDEs
Shanshan Wang, Mamadou Diagne, Miroslav Krstić
DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability
Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, Ruogu Fang
Data augmentation and explainability for bias discovery and mitigation in deep learning
Agnieszka Mikołajczyk-Bareła
Can ultrasound confidence maps predict sonographers' labeling variability?
Vanessa Gonzalez Duque, Leonhard Zirus, Yordanka Velikova, Nassir Navab, Diana Mateus
Deep Learning Techniques in Extreme Weather Events: A Review
Shikha Verma, Kuldeep Srivastava, Akhilesh Tiwari, Shekhar Verma