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
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
Lightning UQ Box: A Comprehensive Framework for Uncertainty Quantification in Deep Learning
Nils Lehmann, Jakob Gawlikowski, Adam J. Stewart, Vytautas Jancauskas, Stefan Depeweg, Eric Nalisnick, Nina Maria Gottschling
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
Keyu Chen, Ziqian Bi, Tianyang Wang, Yizhu Wen, Pohsun Feng, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Caitlyn Heqi Yin, Ming Liu
Accelerating Deep Learning with Fixed Time Budget
Muhammad Asif Khan, Ridha Hamila, Hamid Menouar
DecTrain: Deciding When to Train a DNN Online
Zih-Sing Fu, Soumya Sudhakar, Sertac Karaman, Vivienne Sze
Forecasting Smog Clouds With Deep Learning
Valentijn Oldenburg, Juan Cardenas-Cartagena, Matias Valdenegro-Toro
A Novel Method for Accurate & Real-time Food Classification: The Synergistic Integration of EfficientNetB7, CBAM, Transfer Learning, and Data Augmentation
Shayan Rokhva, Babak Teimourpour