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
Unsupervised Welding Defect Detection Using Audio And Video
Georg Stemmer, Jose A. Lopez, Juan A. Del Hoyo Ontiveros, Arvind Raju, Tara Thimmanaik, Sovan Biswas
Unveiling Deep Shadows: A Survey on Image and Video Shadow Detection, Removal, and Generation in the Era of Deep Learning
Xiaowei Hu, Zhenghao Xing, Tianyu Wang, Chi-Wing Fu, Pheng-Ann Heng
Deep Learning Techniques for Atmospheric Turbulence Removal: A Review
Paul Hill, Nantheera Anantrasirichai, Alin Achim, David Bull
On-chain Validation of Tracking Data Messages (TDM) Using Distributed Deep Learning on a Proof of Stake (PoS) Blockchain
Yasir Latif, Anirban Chowdhury, Samya Bagchi
VLSI Hypergraph Partitioning with Deep Learning
Muhammad Hadir Khan, Bugra Onal, Eren Dogan, Matthew R. Guthaus
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Iulian Emil Tampu, Per Nyman, Christoforos Spyretos, Ida Blystad, Alia Shamikh, Gabriela Prochazka, Teresita Díaz de Ståhl, Johanna Sandgren, Peter Lundberg, Neda Haj-Hosseini
Learning Robust Representations for Communications over Noisy Channels
Sudharsan Senthil, Shubham Paul, Nambi Seshadri, R. David Koilpillai
TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions
Sibo Cheng, Jinyang Min, Che Liu, Rossella Arcucci
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks
Osama Mustafa, Khizer Ali, Talha Naqash
Deep Feature Embedding for Tabular Data
Yuqian Wu, Hengyi Luo, Raymond S. T. Lee
Generative Modeling Perspective for Control and Reasoning in Robotics
Takuma Yoneda
Towards Efficient Modelling of String Dynamics: A Comparison of State Space and Koopman based Deep Learning Methods
Rodrigo Diaz, Carlos De La Vega Martin, Mark Sandler
Subspace Representation Learning for Sparse Linear Arrays to Localize More Sources than Sensors: A Deep Learning Methodology
Kuan-Lin Chen, Bhaskar D. Rao
CNN Based Detection of Cardiovascular Diseases from ECG Images
Irem Sayin, Rana Gursoy, Buse Cicek, Yunus Emre Mert, Fatih Ozturk, Taha Emre Pamukcu, Ceylin Deniz Sevimli, Huseyin Uvet
Integrating Features for Recognizing Human Activities through Optimized Parameters in Graph Convolutional Networks and Transformer Architectures
Mohammad Belal, Taimur Hassan, Abdelfatah Hassan, Nael Alsheikh, Noureldin Elhendawi, Irfan Hussain
ART: Actually Robust Training
Sebastian Chwilczyński, Kacper Trębacz, Karol Cyganik, Mateusz Małecki, Dariusz Brzezinski