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
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
Deep Learning for Options Trading: An End-To-End Approach
Wee Ling Tan, Stephen Roberts, Stefan Zohren
Universal Approximation Theory: Foundations for Parallelism in Neural Networks
Wei Wang, Qing Li
Design and Development of Laughter Recognition System Based on Multimodal Fusion and Deep Learning
Fuzheng Zhao, Yu Bai
Differentially Private Block-wise Gradient Shuffle for Deep Learning
David Zagardo
Exploring Loss Landscapes through the Lens of Spin Glass Theory
Hao Liao, Wei Zhang, Zhanyi Huang, Zexiao Long, Mingyang Zhou, Xiaoqun Wu, Rui Mao, Chi Ho Yeung
Discriminating retinal microvascular and neuronal differences related to migraines: Deep Learning based Crossectional Study
Feilong Tang, Matt Trinh, Annita Duong, Angelica Ly, Fiona Stapleton, Zhe Chen, Zongyuan Ge, Imran Razzak