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 50
September 3, 2024
Unveiling Deep Shadows: A Survey and Benchmark on Image and Video Shadow Detection, Removal, and Generation in the Deep Learning Era
Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
On-chain Validation of Tracking Data Messages (TDM) Using Distributed Deep Learning on a Proof of Stake (PoS) Blockchain
September 2, 2024
September 1, 2024
August 30, 2024
TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks
Deep Feature Embedding for Tabular Data
Generative Modeling Perspective for Control and Reasoning in Robotics
August 29, 2024
Towards Efficient Modelling of String Dynamics: A Comparison of State Space and Koopman based Deep Learning Methods
Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
CNN Based Detection of Cardiovascular Diseases from ECG Images
Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures
ART: Actually Robust Training
Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey