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.
3874papers
Papers - Page 36
October 12, 2024
October 11, 2024
October 10, 2024
Bilinear MLPs enable weight-based mechanistic interpretability
Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10
Physics and Deep Learning in Computational Wave Imaging
Deep Learning for Generalised Planning with Background Knowledge
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
On the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models
Boosting Deep Ensembles with Learning Rate Tuning
October 9, 2024
Exploring the design space of deep-learning-based weather forecasting systems
Enhancing Soccer Camera Calibration Through Keypoint Exploitation
JPEG Inspired Deep Learning
Emergent properties with repeated examples
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods
Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review