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
Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches
Sarasadat Foroughipoor, Kimia Moradi, Hamidreza Bolhasani
Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle
Dániel Rácz, Mihály Petreczky, András Csertán, Bálint Daróczy
Deep Learning on SAR Imagery: Transfer Learning Versus Randomly Initialized Weights
Morteza Karimzadeh, Rafael Pires de Lima
Real-time Neonatal Chest Sound Separation using Deep Learning
Yang Yi Poh, Ethan Grooby, Kenneth Tan, Lindsay Zhou, Arrabella King, Ashwin Ramanathan, Atul Malhotra, Mehrtash Harandi, Faezeh Marzbanrad
This Reads Like That: Deep Learning for Interpretable Natural Language Processing
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt
Trust, but Verify: Robust Image Segmentation using Deep Learning
Fahim Ahmed Zaman, Xiaodong Wu, Weiyu Xu, Milan Sonka, Raghuraman Mudumbai
Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping
Eloy Peña-Asensio, Josep M. Trigo-Rodríguez, Pau Grèbol-Tomàs, David Regordosa-Avellana, Albert Rimola
Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images
Hana Ahmadzadeh Sarhangi, Dorsa Beigifard, Elahe Farmani, Hamidreza Bolhasani
Data Optimization in Deep Learning: A Survey
Ou Wu, Rujing Yao
A Comprehensive Python Library for Deep Learning-Based Event Detection in Multivariate Time Series Data and Information Retrieval in NLP
Menouar Azib, Benjamin Renard, Philippe Garnier, Vincent Génot, Nicolas André
Unknown Health States Recognition With Collective Decision Based Deep Learning Networks In Predictive Maintenance Applications
Chuyue Lou, M. Amine Atoui
Radio Frequency Fingerprinting via Deep Learning: Challenges and Opportunities
Saeif Al-Hazbi, Ahmed Hussain, Savio Sciancalepore, Gabriele Oligeri, Panos Papadimitratos
Learning Efficient Surrogate Dynamic Models with Graph Spline Networks
Chuanbo Hua, Federico Berto, Michael Poli, Stefano Massaroli, Jinkyoo Park
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches
Arun Kumar Silivery, Ram Mohan Rao Kovvur
Deep Learning for Plant Identification and Disease Classification from Leaf Images: Multi-prediction Approaches
Jianping Yao, Son N. Tran, Saurabh Garg, Samantha Sawyer
On the Foundations of Shortcut Learning
Katherine L. Hermann, Hossein Mobahi, Thomas Fel, Michael C. Mozer
Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning
Jon Alvarez Justo, Alexandru Ghita, Daniel Kovac, Joseph L. Garrett, Mariana-Iuliana Georgescu, Jesus Gonzalez-Llorente, Radu Tudor Ionescu, Tor Arne Johansen
Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations
Leonardo Petrini
Deep Learning Models for Classification of COVID-19 Cases by Medical Images
Amir Ali