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
A Learnable Prior Improves Inverse Tumor Growth Modeling
Jonas Weidner, Ivan Ezhov, Michal Balcerak, Marie-Christin Metz, Sergey Litvinov, Sebastian Kaltenbach, Leonhard Feiner, Laurin Lux, Florian Kofler, Jana Lipkova, Jonas Latz, Daniel Rueckert, Bjoern Menze, Benedikt Wiestler
Improved Focus on Hard Samples for Lung Nodule Detection
Yujiang Chen, Mei Xie
SGNet: Folding Symmetrical Protein Complex with Deep Learning
Zhaoqun Li, Jingcheng Yu, Qiwei Ye
Impacts of Color and Texture Distortions on Earth Observation Data in Deep Learning
Martin Willbo, Aleksis Pirinen, John Martinsson, Edvin Listo Zec, Olof Mogren, Mikael Nilsson
Comparison of gait phase detection using traditional machine learning and deep learning techniques
Farhad Nazari, Navid Mohajer, Darius Nahavandi, Abbas Khosravi
Edge-based Parametric Digital Twins for Intelligent Building Indoor Climate Modeling
Zhongjun Ni, Chi Zhang, Magnus Karlsson, Shaofang Gong
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection
Boyang Peng, Sanqing Qu, Yong Wu, Tianpei Zou, Lianghua He, Alois Knoll, Guang Chen, changjun jiang
Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
Mariusz Bujny, Katarzyna Jesionek, Jakub Nalepa, Karol Miszalski-Jamka, Katarzyna Widawka-Żak, Sabina Wolny, Marcin Kostur
Towards efficient deep autoencoders for multivariate time series anomaly detection
Marcin Pietroń, Dominik Żurek, Kamil Faber, Roberto Corizzo
Machine and deep learning methods for predicting 3D genome organization
Brydon P. G. Wall, My Nguyen, J. Chuck Harrell, Mikhail G. Dozmorov
Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Ziwen Wang, Jin Wee Lee, Tanujit Chakraborty, Yilin Ning, Mingxuan Liu, Feng Xie, Marcus Eng Hock Ong, Nan Liu
Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection
Akhila Krishna, Ravi Kant Gupta, Pranav Jeevan, Amit Sethi
AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation
Chenying Liu, Conrad M Albrecht, Yi Wang, Qingyu Li, Xiao Xiang Zhu
Machine Learning vs Deep Learning: The Generalization Problem
Yong Yi Bay, Kathleen A. Yearick
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques
Shivam Pande