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
Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts
Kinya Toride, Matthew Newman, Andrew Hoell, Antonietta Capotondi, Jakob Schlör, Dillon J. Amaya
Direct Zernike Coefficient Prediction from Point Spread Functions and Extended Images using Deep Learning
Yong En Kok, Alexander Bentley, Andrew Parkes, Amanda J. Wright, Michael G. Somekh, Michael Pound
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection
Weixing Wang, Haojin Yang, Christoph Meinel, Hasan Yagiz Özkan, Cristian Bermudez Serna, Carmen Mas-Machuca
Deep Learning as Ricci Flow
Anthony Baptista, Alessandro Barp, Tapabrata Chakraborti, Chris Harbron, Ben D. MacArthur, Christopher R. S. Banerji
Autoencoder-assisted Feature Ensemble Net for Incipient Faults
Mingxuan Gao, Min Wang, Maoyin Chen
A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning
Yu-Xin Zhang, Jie Gui, Xiaofeng Cong, Xin Gong, Wenbing Tao
Minimum Description Feature Selection for Complexity Reduction in Machine Learning-based Wireless Positioning
Myeung Suk Oh, Anindya Bijoy Das, Taejoon Kim, David J. Love, Christopher G. Brinton
A Nasal Cytology Dataset for Object Detection and Deep Learning
Mauro Camporeale, Giovanni Dimauro, Matteo Gelardi, Giorgia Iacobellis, Mattia Sebastiano Ladisa, Sergio Latrofa, Nunzia Lomonte
Utilizing Deep Learning to Optimize Software Development Processes
Keqin Li, Armando Zhu, Peng Zhao, Jintong Song, Jiabei Liu
Cell Phone Image-Based Persian Rice Detection and Classification Using Deep Learning Techniques
Mahmood Saeedi kelishami, Amin Saeidi Kelishami, Sajjad Saeedi Kelishami
Parameter Efficient Fine Tuning: A Comprehensive Analysis Across Applications
Charith Chandra Sai Balne, Sreyoshi Bhaduri, Tamoghna Roy, Vinija Jain, Aman Chadha
Deep Learning-based Text-in-Image Watermarking
Bishwa Karki, Chun-Hua Tsai, Pei-Chi Huang, Xin Zhong
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction
Paulo Henrique dos Santos, Valéria de Carvalho Santos, Eduardo José da Silva Luz
Efficient Learning of Fuzzy Logic Systems for Large-Scale Data Using Deep Learning
Ata Koklu, Yusuf Guven, Tufan Kumbasar
Proteus: Preserving Model Confidentiality during Graph Optimizations
Yubo Gao, Maryam Haghifam, Christina Giannoula, Renbo Tu, Gennady Pekhimenko, Nandita Vijaykumar
DeepLocalization: Using change point detection for Temporal Action Localization
Mohammed Shaiqur Rahman, Ibne Farabi Shihab, Lynna Chu, Anuj Sharma
Learning with 3D rotations, a hitchhiker's guide to SO(3)
A. René Geist, Jonas Frey, Mikel Zobro, Anna Levina, Georg Martius
Deep Learning for Video-Based Assessment of Endotracheal Intubation Skills
Jean-Paul Ainam, Erim Yanik, Rahul Rahul, Taylor Kunkes, Lora Cavuoto, Brian Clemency, Kaori Tanaka, Matthew Hackett, Jack Norfleet, Suvranu De
A Comparison of Traditional and Deep Learning Methods for Parameter Estimation of the Ornstein-Uhlenbeck Process
Jacob Fein-Ashley