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
FAD-SAR: A Novel Fishing Activity Detection System via Synthetic Aperture Radar Images Based on Deep Learning Method
Yanbing Bai, Siao Li, Rui-Yang Ju, Zihao Yang, Jinze Yu, Jen-Shiun Chiang
Enhancing Computational Efficiency in Multiscale Systems Using Deep Learning of Coordinates and Flow Maps
Asif Hamid, Danish Rafiq, Shahkar Ahmad Nahvi, Mohammad Abid Bazaz
Application of Deep Learning for Factor Timing in Asset Management
Prabhu Prasad Panda, Maysam Khodayari Gharanchaei, Xilin Chen, Haoshu Lyu
Deep Learning for Low-Latency, Quantum-Ready RF Sensing
Pranav Gokhale, Caitlin Carnahan, William Clark, Frederic T. Chong
PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis
Md Robiul Islam, Md Mahamodul Islam, Mst. Suraiya Afrin, Anika Antara, Nujhat Tabassum, Al Amin
A Survey of Deep Learning Library Testing Methods
Xiaoyu Zhang, Weipeng Jiang, Chao Shen, Qi Li, Qian Wang, Chenhao Lin, Xiaohong Guan
Deep Learning for Melt Pool Depth Contour Prediction From Surface Thermal Images via Vision Transformers
Francis Ogoke, Peter Myung-Won Pak, Alexander Myers, Guadalupe Quirarte, Jack Beuth, Jonathan Malen, Amir Barati Farimani
Validating Deep-Learning Weather Forecast Models on Recent High-Impact Extreme Events
Olivier C. Pasche, Jonathan Wider, Zhongwei Zhang, Jakob Zscheischler, Sebastian Engelke
Baseline Drift Tolerant Signal Encoding for ECG Classification with Deep Learning
Robert O Shea, Prabodh Katti, Bipin Rajendran
Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques
Ziliang Hong, Debesh Jha, Koushik Biswas, Zheyuan Zhang, Yury Velichko, Cemal Yazici, Temel Tirkes, Amir Borhani, Baris Turkbey, Alpay Medetalibeyoglu, Gorkem Durak, Ulas Bagci
A Novel Machine Learning-based Equalizer for a Downstream 100G PAM-4 PON
Chen Shao, Elias Giacoumidis, Shi Li, Jialei Li, Michael Faerber, Tobias Kaefer, Andre Richter
On-the-fly Data Augmentation for Forecasting with Deep Learning
Vitor Cerqueira, Moisés Santos, Yassine Baghoussi, Carlos Soares
Research on geometric figure classification algorithm based on Deep Learning
Ruiyang Wang, Haonan Wang, Junfeng Sun, Mingjia Zhao, Meng Liu
SoK: Behind the Accuracy of Complex Human Activity Recognition Using Deep Learning
Duc-Anh Nguyen, Nhien-An Le-Khac
Efficient Higher-order Convolution for Small Kernels in Deep Learning
Zuocheng Wen, Lingzhong Guo
Improved impedance inversion by deep learning and iterated graph Laplacian
Davide Bianchi, Florian Bossmann, Wenlong Wang, Mingming Liu
Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory
Monibor Rahman, Adam Carpenter, Khan Iftekharuddin, Chris Tennant
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review
Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron
A Survey of Deep Long-Tail Classification Advancements
Charika de Alvis, Suranga Seneviratne