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
Benchmarking and Enhancing Surgical Phase Recognition Models for Robotic-Assisted Esophagectomy
Yiping Li, Romy van Jaarsveld, Ronald de Jong, Jasper Bongers, Gino Kuiper, Richard van Hillegersberg, Jelle Ruurda, Marcel Breeuwer, Yasmina Al Khalil
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking
Shahran Rahman Alve
Deep Learning Modeling Method for RF Devices Based on Uniform Noise Training Set
Zhaokun Hu, Yindong Xiao, Houjun Wang, Jiayong Yu, Zihang Gao
Using SlowFast Networks for Near-Miss Incident Analysis in Dashcam Videos
Yucheng Zhang, Koichi Emura, Eiji Watanabe
Dual-Branch Subpixel-Guided Network for Hyperspectral Image Classification
Zhu Han, Jin Yang, Lianru Gao, Zhiqiang Zeng, Bing Zhang, Jocelyn Chanussot
Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion
Andrea Asperti, Ali Aydogdu, Emanuela Clementi, Angelo Greco, Lorenzo Mentaschi, Fabio Merizzi, Pietro Miraglio, Paolo Oddo, Nadia Pinardi, Alessandro Testa
Deep Learning in Single-Cell and Spatial Transcriptomics Data Analysis: Advances and Challenges from a Data Science Perspective
Shuang Ge, Shuqing Sun, Huan Xu, Qiang Cheng, Zhixiang Ren
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness
Vincent Abbott, Gioele Zardini
Few-Shot Learning with Adaptive Weight Masking in Conditional GANs
Jiacheng Hu, Zhen Qi, Jianjun Wei, Jiajing Chen, Runyuan Bao, Xinyu Qiu
An ADHD Diagnostic Interface Based on EEG Spectrograms and Deep Learning Techniques
Medha Pappula, Syed Muhammad Anwar
Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun, Yu Luo, Ling Wang, Siyuan Wang, Na Guo, Lei Liu, Tian Zhao, Xinzhou Wang, Lei Yang, Shuo Jin, Jun Yan, Jiahong Dong
OMENN: One Matrix to Explain Neural Networks
Adam Wróbel, Mikołaj Janusz, Bartosz Zieliński, Dawid Rymarczyk
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management
Weiche Hsieh, Ziqian Bi, Keyu Chen, Benji Peng, Sen Zhang, Jiawei Xu, Jinlang Wang, Caitlyn Heqi Yin, Yichao Zhang, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Chia Xin Liang, Jintao Ren, Qian Niu, Silin Chen, Lawrence K.Q. Yan, Han Xu, Hong-Ming Tseng, Xinyuan Song, Bowen Jing, Junjie Yang, Junhao Song, Junyu Liu, Ming Liu
Performance Comparison of Deep Learning Techniques in Naira Classification
Ismail Ismail Tijjani, Ahmad Abubakar Mustapha, Isma'il Tijjani Idris
Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
Sebastian Peitz, Sedjro Salomon Hotegni
A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles
Linh Trinh, Siegfried Mercelis, Ali Anwar
A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
Pang Li, Shahrul Azman Mohd Noah, Hafiz Mohd Sarim
AdaScale: Dynamic Context-aware DNN Scaling via Automated Adaptation Loop on Mobile Devices
Yuzhan Wang, Sicong Liu, Bin Guo, Boqi Zhang, Ke Ma, Yasan Ding, Hao Luo, Yao Li, Zhiwen Yu
Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer
Xin Tie, Weijie Chen, Zachary Huemann, Brayden Schott, Nuohao Liu, Tyler J. Bradshaw