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
Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function
Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee, Oscar Barrera, Benjamin Geisler, Peter J. Hirschfeld, Richard G. Hennig
Deep Learning for Multi-Label Learning: A Comprehensive Survey
Adane Nega Tarekegn, Mohib Ullah, Faouzi Alaya Cheikh
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
Lei Xu, Mete Ahishali, Moncef Gabbouj
A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography
Yuzhong Zhou, Linda-Sophie Schneider, Fuxin Fan, Andreas Maier
Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19
Gautam Siddharth Kashyap, Jatin Sohlot, Ayesha Siddiqui, Ramsha Siddiqui, Karan Malik, Samar Wazir, Alexander E. I. Brownlee
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopes
Riccardo Crupi
Scalable Glacier Mapping using Deep Learning and Open Earth Observation Data Matches the Accuracy of Manual Delineation
Konstantin A. Maslov, Claudio Persello, Thomas Schellenberger, Alfred Stein
At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition
Ashok Dahal, Raphaël Huser, Luigi Lombardo
Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease
Yihao Lin, Ximeng Li, Yan Zhang, Jinshan Tang
Deep Learning Innovations in Diagnosing Diabetic Retinopathy: The Potential of Transfer Learning and the DiaCNN Model
Mohamed R. Shoaib, Heba M. Emara, Jun Zhao, Walid El-Shafai, Naglaa F. Soliman, Ahmed S. Mubarak, Osama A. Omer, Fathi E. Abd El-Samie, Hamada Esmaiel
Transfer Learning With Densenet201 Architecture Model For Potato Leaf Disease Classification
Rifqi Alfinnur Charisma, Faisal Dharma Adhinata
A Survey of Deep Learning and Foundation Models for Time Series Forecasting
John A. Miller, Mohammed Aldosari, Farah Saeed, Nasid Habib Barna, Subas Rana, I. Budak Arpinar, Ninghao Liu
Deep Learning for Improved Polyp Detection from Synthetic Narrow-Band Imaging
Mathias Ramm Haugland, Hemin Ali Qadir, Ilangko Balasingham
Self-Improving Interference Management Based on Deep Learning With Uncertainty Quantification
Hyun-Suk Lee, Do-Yup Kim, Kyungsik Min
Predicting Mitral Valve mTEER Surgery Outcomes Using Machine Learning and Deep Learning Techniques
Tejas Vyas, Mohsena Chowdhury, Xiaojiao Xiao, Mathias Claeys, Géraldine Ong, Guanghui Wang