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

September 3, 2023

Multidomain transformer-based deep learning for early detection of network intrusion
Jinxin Liu, Murat Simsek, Michele Nogueira, Burak Kantarci
Deep Learning Automatic Detection Multi Domain Network Intrusion Detection Early Detection Flow Based Network Flow Network Intrusion

September 2, 2023

September 1, 2023

August 31, 2023

August 29, 2023

Deep Learning

Papers

Multidomain transformer-based deep learning for early detection of network intrusion

Deep-Learning Framework for Optimal Selection of Soil Sampling Sites

Short-term power load forecasting method based on CNN-SAEDN-Res

Deep Learning and Inverse Problems

Deep learning in medical image registration: introduction and survey

Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning

Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques

Bellybutton: Accessible and Customizable Deep-Learning Image Segmentation

Efficacy of Neural Prediction-Based Zero-Shot NAS

Proof of Deep Learning: Approaches, Challenges, and Future Directions

Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting

Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing

Privacy-Preserving Medical Image Classification through Deep Learning and Matrix Decomposition

From Pixels to Portraits: A Comprehensive Survey of Talking Head Generation Techniques and Applications

Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations

Multimodal Recommender Systems in the Prediction of Disease Comorbidity

Gradient-based methods for spiking physical systems

Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata

Uncertainty Aware Training to Improve Deep Learning Model Calibration for Classification of Cardiac MR Images