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
Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review
Mahtab Faraji, Homa Rashidisabet, George R. Nahass, RV Paul Chan, Thasarat S Vajaranant, Darvin Yi
Plasticity Loss in Deep Reinforcement Learning: A Survey
Timo Klein, Lukas Miklautz, Kevin Sidak, Claudia Plant, Sebastian Tschiatschek
Rethinking Deep Learning: Non-backpropagation and Non-optimization Machine Learning Approach Using Hebbian Neural Networks
Kei Itoh
Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic
Sharvani Srivastava, Sudhakar Singh, Pooja, Shiv Prakash
Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method
Yunfan Zhang, Zekun Niu, Minghui Shi, Weisheng Hu, Lilin Yi
Saliency Assisted Quantization for Neural Networks
Elmira Mousa Rezabeyk, Salar Beigzad, Yasin Hamzavi, Mohsen Bagheritabar, Seyedeh Sogol Mirikhoozani
Model and Deep learning based Dynamic Range Compression Inversion
Haoran Sun, Dominique Fourer, Hichem Maaref
Deep Heuristic Learning for Real-Time Urban Pathfinding
Mohamed Hussein Abo El-Ela, Ali Hamdi Fergany
Are Deep Learning Methods Suitable for Downscaling Global Climate Projections? Review and Intercomparison of Existing Models
Jose González-Abad, José Manuel Gutiérrez
Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis
Alexandros Gkillas, Aris Lalos
An Edge Computing-Based Solution for Real-Time Leaf Disease Classification using Thermal Imaging
Públio Elon Correa da Silva, Jurandy Almeida
Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Shan Zhao, Zhaiyu Chen, Zhitong Xiong, Yilei Shi, Sudipan Saha, Xiao Xiang Zhu
Navigating Distribution Shifts in Medical Image Analysis: A Survey
Zixian Su, Jingwei Guo, Xi Yang, Qiufeng Wang, Frans Coenen, Kaizhu Huang
ADOPT: Modified Adam Can Converge with Any $β_2$ with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi, Yuta Oshima, Seong Cheol Jeong, Go Nagahara, Tomoshi Iiyama, Masahiro Suzuki, Yusuke Iwasawa, Yutaka Matsuo
DeepContext: A Context-aware, Cross-platform, and Cross-framework Tool for Performance Profiling and Analysis of Deep Learning Workloads
Qidong Zhao, Hao Wu, Yuming Hao, Zilingfeng Ye, Jiajia Li, Xu Liu, Keren Zhou
Advancing Recycling Efficiency: A Comparative Analysis of Deep Learning Models in Waste Classification
Zhanshan Qiao
Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning
Toros Arikan, Luca M. Chackalackal, Fatima Ahsan, Konrad Tittel, Andrew C. Singer, Gregory W. Wornell, Richard G. Baraniuk
Deep Learning on 3D Semantic Segmentation: A Detailed Review
Thodoris Betsas, Andreas Georgopoulos, Anastasios Doulamis, Pierre Grussenmeyer
Towards certification: A complete statistical validation pipeline for supervised learning in industry
Lucas Lacasa, Abel Pardo, Pablo Arbelo, Miguel Sánchez, Pablo Yeste, Noelia Bascones, Alejandro Martínez-Cava, Gonzalo Rubio, Ignacio Gómez, Eusebio Valero, Javier de Vicente