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
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 (CESBIO, UO), David Morin (CESBIO), Milena Planells (CESBIO), 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
More Than Positive and Negative: Communicating Fine Granularity in Medical Diagnosis
Xiangyu Peng, Kai Wang, Jianfei Yang, Yingying Zhu, Yang You
Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives
Lei Ma, Ziyun Yan, Mengmeng Li, Tao Liu, Liqin Tan, Xuan Wang, Weiqiang He, Ruikun Wang, Guangjun He, Heng Lu, Thomas Blaschke
Enhanced Knee Kinematics: Leveraging Deep Learning and Morphing Algorithms for 3D Implant Modeling
Viet-Dung Nguyen, Michael T. LaCour, Richard D. Komistek
Gradient flow in parameter space is equivalent to linear interpolation in output space
Thomas Chen, Patrícia Muñoz Ewald
NeuralBeta: Estimating Beta Using Deep Learning
Yuxin Liu, Jimin Lin, Achintya Gopal
Deep Learning based Visually Rich Document Content Understanding: A Survey
Yihao Ding, Jean Lee, Soyeon Caren Han
A Survey of Mamba
Haohao Qu, Liangbo Ning, Rui An, Wenqi Fan, Tyler Derr, Hui Liu, Xin Xu, Qing Li
Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images
Xiaoyi Liu, Zhuoyue Wang
Regional quality estimation for echocardiography using deep learning
Gilles Van De Vyver, Svein-Erik Måsøy, Håvard Dalen, Bjørnar Leangen Grenne, Espen Holte, Sindre Hellum Olaisen, John Nyberg, Andreas Østvik, Lasse Løvstakken, Erik Smistad
What comes after transformers? -- A selective survey connecting ideas in deep learning
Johannes Schneider
Revocable Backdoor for Deep Model Trading
Yiran Xu, Nan Zhong, Zhenxing Qian, Xinpeng Zhang
Discovering Car-following Dynamics from Trajectory Data through Deep Learning
Ohay Angah, James Enouen, Xuegang, Ban, Yan Liu