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
Advancing Pavement Distress Detection in Developing Countries: A Novel Deep Learning Approach with Locally-Collected Datasets
Blessing Agyei Kyem, Eugene Kofi Okrah Denteh, Joshua Kofi Asamoah, Kenneth Adomako Tutu, Armstrong Aboah
Quantum-secure multiparty deep learning
Kfir Sulimany, Sri Krishna Vadlamani, Ryan Hamerly, Prahlad Iyengar, Dirk Englund
On the Geometry of Deep Learning
Randall Balestriero, Ahmed Imtiaz Humayun, Richard Baraniuk
Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis
Dilek M. Yalcinkaya, Khalid Youssef, Bobak Heydari, Janet Wei, Noel Bairey Merz, Robert Judd, Rohan Dharmakumar, Orlando P. Simonetti, Jonathan W. Weinsaft, Subha V. Raman, Behzad Sharif
Segmentation of Mental Foramen in Orthopantomographs: A Deep Learning Approach
Haider Raza, Mohsin Ali, Vishal Krishna Singh, Agustin Wahjuningrum, Rachel Sarig, Akhilanand Chaurasia
Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis
Michele Craighero, Sarah Solbiati, Federica Mozzini, Enrico Caiani, Giacomo Boracchi
Clutter Classification Using Deep Learning in Multiple Stages
Ryan Dempsey, Jonathan Ethier
Deep Transfer Learning for Kidney Cancer Diagnosis
Yassine Habchi, Hamza Kheddar, Yassine Himeur, Abdelkrim Boukabou, Shadi Atalla, Wathiq Mansoor, Hussain Al-Ahmad
Combining Neural Architecture Search and Automatic Code Optimization: A Survey
Inas Bachiri, Hadjer Benmeziane, Smail Niar, Riyadh Baghdadi, Hamza Ouarnoughi, Abdelkrime Aries
Hate Speech Detection and Classification in Amharic Text with Deep Learning
Samuel Minale Gashe, Seid Muhie Yimam, Yaregal Assabie
Anatomical Foundation Models for Brain MRIs
Carlo Alberto Barbano, Matteo Brunello, Benoit Dufumier, Marco Grangetto
A comparative study of generative adversarial networks for image recognition algorithms based on deep learning and traditional methods
Yihao Zhong, Yijing Wei, Yingbin Liang, Xiqing Liu, Rongwei Ji, Yiru Cang
Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8
Fan Zhao, Yijia Chen, Dianhan Xi, Yongying Liu, Jiaqi Wang, Shigeru Tabeta, Katsunori Mizuno
Adaptive Friction in Deep Learning: Enhancing Optimizers with Sigmoid and Tanh Function
Hongye Zheng, Bingxing Wang, Minheng Xiao, Honglin Qin, Zhizhong Wu, Lianghao Tan
Post-Mortem Human Iris Segmentation Analysis with Deep Learning
Afzal Hossain, Tipu Sultan, Stephanie Schuckers
Enabling Intelligent Traffic Systems: A Deep Learning Method for Accurate Arabic License Plate Recognition
M. A. Sayedelahl
A Metric Driven Approach to Mixed Precision Training
Mitchelle Rasquinha, Gil Tabak
Tree species classification at the pixel-level using deep learning and multispectral time series in an imbalanced context
Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux
Attenuation-adjusted deep learning of pore defects in 2D radiographs of additive manufacturing powders
Andreas Bjerregaard, David Schumacher, Jon Sporring
Scribble-Based Interactive Segmentation of Medical Hyperspectral Images
Zhonghao Wang, Junwen Wang, Charlie Budd, Oscar MacCormac, Jonathan Shapey, Tom Vercauteren