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
Learning Algorithms Made Simple
Noorbakhsh Amiri Golilarz, Elias Hossain, Abdoljalil Addeh, Keyan Alexander Rahimi
HyperPg -- Prototypical Gaussians on the Hypersphere for Interpretable Deep Learning
Maximilian Xiling Li, Korbinian Franz Rudolf, Nils Blank, Rudolf Lioutikov
Multi-modal Fusion based Q-distribution Prediction for Controlled Nuclear Fusion
Shiao Wang, Yifeng Wang, Qingchuan Ma, Xiao Wang, Ning Yan, Qingquan Yang, Guosheng Xu, Jin Tang
Bilinear MLPs enable weight-based mechanistic interpretability
Michael T. Pearce, Thomas Dooms, Alice Rigg, Jose M. Oramas, Lee Sharkey
Optimizing YOLO Architectures for Optimal Road Damage Detection and Classification: A Comparative Study from YOLOv7 to YOLOv10
Vung Pham, Lan Dong Thi Ngoc, Duy-Linh Bui
Physics and Deep Learning in Computational Wave Imaging
Youzuo Lin, Shihang Feng, James Theiler, Yinpeng Chen, Umberto Villa, Jing Rao, John Greenhall, Cristian Pantea, Mark A. Anastasio, Brendt Wohlberg
Deep Learning for Generalised Planning with Background Knowledge
Dillon Z. Chen, Rostislav Horčík, Gustav Šír
Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey
Zihan Yu, Tianxiao Li, Yuxin Zhu, Rongze Pan
On the Generalization Properties of Deep Learning for Aircraft Fuel Flow Estimation Models
Gabriel Jarry, Ramon Dalmau, Philippe Very, Junzi Sun
Boosting Deep Ensembles with Learning Rate Tuning
Hongpeng Jin, Yanzhao Wu
Exploring the design space of deep-learning-based weather forecasting systems
Shoaib Ahmed Siddiqui, Jean Kossaifi, Boris Bonev, Christopher Choy, Jan Kautz, David Krueger, Kamyar Azizzadenesheli
Enhancing Soccer Camera Calibration Through Keypoint Exploitation
Nikolay S. Falaleev, Ruilong Chen
JPEG Inspired Deep Learning
Ahmed H. Salamah, Kaixiang Zheng, Yiwen Liu, En-Hui Yang
Emergent properties with repeated examples
François Charton, Julia Kempe
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods
Lise Le Boudec, Emmanuel de Bezenac, Louis Serrano, Ramon Daniel Regueiro-Espino, Yuan Yin, Patrick Gallinari
Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review
Fatimaelzahraa Ali Ahmed, Mahmoud Yousef, Mariam Ali Ahmed, Hasan Omar Ali, Anns Mahboob, Hazrat Ali, Zubair Shah, Omar Aboumarzouk, Abdulla Al Ansari, Shidin Balakrishnan
Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering
Hadas Abraham, Barak Gahtan, Adir Kobovich, Orian Leitersdorf, Alex M. Bronstein, Eitan Yaakobi
Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
Barak Gahtan, Robert J. Shahla, Reuven Cohen, Alex M. Bronstein
Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
Giovanni Messuti, ortensia Amoroso, Ferdinando Napolitano, Mariarosaria Falanga, Paolo Capuano, Silvia Scarpetta
Data Quality Issues in Vulnerability Detection Datasets
Yuejun Guo, Seifeddine Bettaieb