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
Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey
Laniqng Guo, Chong Wang, Yufei Wang, Siyu Huang, Wenhan Yang, Alex C. Kot, Bihan Wen
Deep Learning for Network Anomaly Detection under Data Contamination: Evaluating Robustness and Mitigating Performance Degradation
D'Jeff K. Nkashama, Jordan Masakuna Félicien, Arian Soltani, Jean-Charles Verdier, Pierre-Martin Tardif, Marc Frappier, Froduald Kabanza
How Deep is your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
Linglong Qian, Tao Wang, Jun Wang, Hugh Logan Ellis, Robin Mitra, Richard Dobson, Zina Ibrahim
Approximating G(t)/GI/1 queues with deep learning
Eliran Sherzer, Opher Baron, Dmitry Krass, Yehezkel Resheff
Improving Dental Diagnostics: Enhanced Convolution with Spatial Attention Mechanism
Shahriar Rezaie, Neda Saberitabar, Elnaz Salehi
Estimating the stability number of a random graph using convolutional neural networks
Randy Davila
A Survey on Deep Stereo Matching in the Twenties
Fabio Tosi, Luca Bartolomei, Matteo Poggi
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício, Hugo Ferreira, Pedro Ribeiro, Pedro Bizarro
Stable Weight Updating: A Key to Reliable PDE Solutions Using Deep Learning
A. Noorizadegan, R. Cavoretto, D. L. Young, C. S. Chen
On the power of data augmentation for head pose estimation
Michael Welter
Leveraging Topological Guidance for Improved Knowledge Distillation
Eun Som Jeon, Rahul Khurana, Aishani Pathak, Pavan Turaga
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Ankita Shukla, Yuan Wang, Hyunglae Lee, Matthew P. Buman, Pavan Turaga
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond
Abhilash Khuntia, Shubham Kale
Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE)
Artzai Picon, Pablo Galan, Arantza Bereciartua-Perez, Leire Benito-del-Valle
Deep learning architectures for data-driven damage detection in nonlinear dynamic systems
Harrish Joseph, Giuseppe Quaranta, Biagio Carboni, Walter Lacarbonara
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
Kai Norman Clasen, Leonard Hackel, Tom Burgert, Gencer Sumbul, Begüm Demir, Volker Markl
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Shiyi Wang, Yang Nan, Sheng Zhang, Federico Felder, Xiaodan Xing, Yingying Fang, Javier Del Ser, Simon L F Walsh, Guang Yang
Accelerated Proton Resonance Frequency-based Magnetic Resonance Thermometry by Optimized Deep Learning Method
Sijie Xu, Shenyan Zong, Chang-Sheng Mei, Guofeng Shen, Yueran Zhao, He Wang