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
Alzheimer's Magnetic Resonance Imaging Classification Using Deep and Meta-Learning Models
Nida Nasir, Muneeb Ahmed, Neda Afreen, Mustafa Sameer
Transfer Learning for CSI-based Positioning with Multi-environment Meta-learning
Anastasios Foliadis, Mario H. Castañeda, Richard A. Stirling-Gallacher, Reiner S. Thomä
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks
Taiyuan Mei, Yun Zi, Xiaohan Cheng, Zijun Gao, Qi Wang, Haowei Yang
Automatic segmentation of Organs at Risk in Head and Neck cancer patients from CT and MRI scans
Sébastien Quetin, Andrew Heschl, Mauricio Murillo, Rohit Murali, Shirin A. Enger, Farhad Maleki
Eddeep: Fast eddy-current distortion correction for diffusion MRI with deep learning
Antoine Legouhy, Ross Callaghan, Whitney Stee, Philippe Peigneux, Hojjat Azadbakht, Hui Zhang
Enhancing the analysis of murine neonatal ultrasonic vocalizations: Development, evaluation, and application of different mathematical models
Rudolf Herdt, Louisa Kinzel, Johann Georg Maaß, Marvin Walther, Henning Fröhlich, Tim Schubert, Peter Maass, Christian Patrick Schaaf
Vision-Based Neurosurgical Guidance: Unsupervised Localization and Camera-Pose Prediction
Gary Sarwin, Alessandro Carretta, Victor Staartjes, Matteo Zoli, Diego Mazzatenta, Luca Regli, Carlo Serra, Ender Konukoglu
Deep Blur Multi-Model (DeepBlurMM) -- a strategy to mitigate the impact of image blur on deep learning model performance in histopathology image analysis
Yujie Xiang, Bojing Liu, Mattias Rantalainen
Training Deep Learning Models with Hybrid Datasets for Robust Automatic Target Detection on real SAR images
Benjamin Camus, Théo Voillemin, Corentin Le Barbu, Jean-Christophe Louvigné, Carole Belloni, Emmanuel Vallée
AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning
Zhuoying Li, Bohua Wan, Cong Mu, Ruzhang Zhao, Shushan Qiu, Chao Yan
Deep Learning in Earthquake Engineering: A Comprehensive Review
Yazhou Xie
Analysis of the rate of convergence of an over-parametrized convolutional neural network image classifier learned by gradient descent
Michael Kohler, Adam Krzyzak, Benjamin Walter
Dehazing Remote Sensing and UAV Imagery: A Review of Deep Learning, Prior-based, and Hybrid Approaches
Gao Yu Lee, Jinkuan Chen, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, Vu N Duong