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
Transfer Learning in Human Activity Recognition: A Survey
Sourish Gunesh Dhekane, Thomas Ploetz
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
Holger Boche, Adalbert Fono, Gitta Kutyniok
Ventricular Segmentation: A Brief Comparison of U-Net Derivatives
Ketan Suhaas Saichandran
Automatic 3D Multi-modal Ultrasound Segmentation of Human Placenta using Fusion Strategies and Deep Learning
Sonit Singh, Gordon Stevenson, Brendan Mein, Alec Welsh, Arcot Sowmya
Deep learning enhanced mixed integer optimization: Learning to reduce model dimensionality
Niki Triantafyllou, Maria M. Papathanasiou
Online Stability Improvement of Groebner Basis Solvers using Deep Learning
Wanting Xu, Lan Hu, Manolis C. Tsakiris, Laurent Kneip
Exploring the Role of Convolutional Neural Networks (CNN) in Dental Radiography Segmentation: A Comprehensive Systematic Literature Review
Walid Brahmi, Imen Jdey, Fadoua Drira
Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy
Ruiqing Sun, Delong Yang, Shaohui Zhang, Qun Hao
Deep Ensemble Shape Calibration: Multi-Field Post-hoc Calibration in Online Advertising
Shuai Yang, Hao Yang, Zhuang Zou, Linhe Xu, Shuo Yuan, Yifan Zeng
Probabilistically Robust Watermarking of Neural Networks
Mikhail Pautov, Nikita Bogdanov, Stanislav Pyatkin, Oleg Rogov, Ivan Oseledets
Enhancing Wind Speed and Wind Power Forecasting Using Shape-Wise Feature Engineering: A Novel Approach for Improved Accuracy and Robustness
Mulomba Mukendi Christian, Yun Seon Kim, Hyebong Choi, Jaeyoung Lee, SongHee You
Toward Clinically Trustworthy Deep Learning: Applying Conformal Prediction to Intracranial Hemorrhage Detection
Cooper Gamble, Shahriar Faghani, Bradley J. Erickson
Fusing Echocardiography Images and Medical Records for Continuous Patient Stratification
Nathan Painchaud, Pierre-Yves Courand, Pierre-Marc Jodoin, Nicolas Duchateau, Olivier Bernard
DeepThalamus: A novel deep learning method for automatic segmentation of brain thalamic nuclei from multimodal ultra-high resolution MRI
Marina Ruiz-Perez, Sergio Morell-Ortega, Marien Gadea, Roberto Vivo-Hernando, Gregorio Rubio, Fernando Aparici, Mariam de la Iglesia-Vaya, Thomas Tourdias, Pierrick Coupé, José V. Manjón
Curriculum for Crowd Counting -- Is it Worthy?
Muhammad Asif Khan, Hamid Menouar, Ridha Hamila
Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G
Ruijin Sun, Nan Cheng, Changle Li, Fangjiong Chen, Wen Chen
Your Instructions Are Not Always Helpful: Assessing the Efficacy of Instruction Fine-tuning for Software Vulnerability Detection
Imam Nur Bani Yusuf, Lingxiao Jiang