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
Experimental Assessment of a Forward-Collision Warning System Fusing Deep Learning and Decentralized Radio Sensing
Jorge D. Cardenas, Omar Contreras-Ponce, Carlos A. Gutierrez, Ruth Aguilar-Ponce, Francisco R. Castillo-Soria, Cesar A. Azurdia-Meza
TreeLearn: A Comprehensive Deep Learning Method for Segmenting Individual Trees from Ground-Based LiDAR Forest Point Clouds
Jonathan Henrich, Jan van Delden, Dominik Seidel, Thomas Kneib, Alexander Ecker
Applying Deep Learning to Calibrate Stochastic Volatility Models
Abir Sridi, Paul Bilokon
Towards a universal mechanism for successful deep learning
Yuval Meir, Yarden Tzach, Shiri Hodassman, Ofek Tevet, Ido Kanter
Multi-Grade Deep Learning for Partial Differential Equations with Applications to the Burgers Equation
Yuesheng Xu, Taishan Zeng
Beta quantile regression for robust estimation of uncertainty in the presence of outliers
Haleh Akrami, Omar Zamzam, Anand Joshi, Sergul Aydore, Richard Leahy
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning
Alexander Bastounis, Alexander N. Gorban, Anders C. Hansen, Desmond J. Higham, Danil Prokhorov, Oliver Sutton, Ivan Y. Tyukin, Qinghua Zhou
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
Tilahun M. Getu, Georges Kaddoum
Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances
Xiangrong Zhang, Tianyang Zhang, Guanchun Wang, Peng Zhu, Xu Tang, Xiuping Jia, Licheng Jiao
Improving the Performance of R17 Type-II Codebook with Deep Learning
Ke Ma, Yiliang Sang, Yang Ming, Jin Lian, Chang Tian, Zhaocheng Wang
LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images
Jonathan Fhima, Jan Van Eijgen, Hana Kulenovic, Valérie Debeuf, Marie Vangilbergen, Marie-Isaline Billen, Heloïse Brackenier, Moti Freiman, Ingeborg Stalmans, Joachim A. Behar
A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)
Vedanth Ramanathan, Krish Mahadevan, Sejal Dua
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R. Durran, Raul A. Moreno, Thorsten Kurth, Boris Bonev, Noah Brenowitz, Martin V. Butz
Stream-based Active Learning by Exploiting Temporal Properties in Perception with Temporal Predicted Loss
Sebastian Schmidt, Stephan Günnemann