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
Learning a Neural Solver for Parametric PDE to Enhance Physics-Informed Methods
Lise Le Boudec, Emmanuel de Bezenac, Louis Serrano, Ramon Daniel Regueiro-Espino, Yuan Yin, Patrick Gallinari
Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review
Fatimaelzahraa Ali Ahmed, Mahmoud Yousef, Mariam Ali Ahmed, Hasan Omar Ali, Anns Mahboob, Hazrat Ali, Zubair Shah, Omar Aboumarzouk, Abdulla Al Ansari, Shidin Balakrishnan
Beyond the Alphabet: Deep Signal Embedding for Enhanced DNA Clustering
Hadas Abraham, Barak Gahtan, Adir Kobovich, Orian Leitersdorf, Alex M. Bronstein, Eitan Yaakobi
Estimating the Number of HTTP/3 Responses in QUIC Using Deep Learning
Barak Gahtan, Robert J. Shahla, Reuven Cohen, Alex M. Bronstein
Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
Giovanni Messuti, ortensia Amoroso, Ferdinando Napolitano, Mariarosaria Falanga, Paolo Capuano, Silvia Scarpetta
Data Quality Issues in Vulnerability Detection Datasets
Yuejun Guo, Seifeddine Bettaieb
Deep neural network-based detection of counterfeit products from smartphone images
Hugo Garcia-Cotte, Dorra Mellouli, Abdul Rehman, Li Wang, David G. Stork
A second-order-like optimizer with adaptive gradient scaling for deep learning
Jérôme Bolte (TSE-R), Ryan Boustany (TSE-R), Edouard Pauwels (TSE-R, IRIT-ADRIA), Andrei Purica
Extended convexity and smoothness and their applications in deep learning
Binchuan Qi
Wolf2Pack: The AutoFusion Framework for Dynamic Parameter Fusion
Bowen Tian, Songning Lai, Yutao Yue
Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future
Long Chen, Yuzhi Huang, Junyu Dong, Qi Xu, Sam Kwong, Huimin Lu, Huchuan Lu, Chongyi Li
Improved deep learning of chaotic dynamical systems with multistep penalty losses
Dibyajyoti Chakraborty, Seung Whan Chung, Ashesh Chattopadhyay, Romit Maulik
GAS-Norm: Score-Driven Adaptive Normalization for Non-Stationary Time Series Forecasting in Deep Learning
Edoardo Urettini, Daniele Atzeni, Reshawn J. Ramjattan, Antonio Carta
End-to-End Reaction Field Energy Modeling via Deep Learning based Voxel-to-voxel Transform
Yongxian Wu, Qiang Zhu, Ray Luo
ConceptLens: from Pixels to Understanding
Abhilekha Dalal, Pascal Hitzler