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
Fix-Con: Automatic Fault Localization and Repair of Deep Learning Model Conversions between Frameworks
Nikolaos Louloudakis, Perry Gibson, José Cano, Ajitha Rajan
Deep Learning for Efficient GWAS Feature Selection
Kexuan Li
DRStageNet: Deep Learning for Diabetic Retinopathy Staging from Fundus Images
Yevgeniy Men, Jonathan Fhima, Leo Anthony Celi, Lucas Zago Ribeiro, Luis Filipe Nakayama, Joachim A. Behar
Deep Learning on 3D Neural Fields
Pierluigi Zama Ramirez, Luca De Luigi, Daniele Sirocchi, Adriano Cardace, Riccardo Spezialetti, Francesco Ballerini, Samuele Salti, Luigi Di Stefano
Underwater Acoustic Signal Recognition Based on Salient Feature
Minghao Chen
Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms
Bo Liu, Liqiang Yu, Chang Che, Qunwei Lin, Hao Hu, Xinyu Zhao
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
Anzhe Cheng, Zhenkun Wang, Chenzhong Yin, Mingxi Cheng, Heng Ping, Xiongye Xiao, Shahin Nazarian, Paul Bogdan
Object Detection for Automated Coronary Artery Using Deep Learning
Hadis Keshavarz, Hossein Sadr
MineObserver 2.0: A Deep Learning & In-Game Framework for Assessing Natural Language Descriptions of Minecraft Imagery
Jay Mahajan, Samuel Hum, Jack Henhapl, Diya Yunus, Matthew Gadbury, Emi Brown, Jeff Ginger, H. Chad Lane
Deep Learning Approaches for Seizure Video Analysis: A Review
David Ahmedt-Aristizabal, Mohammad Ali Armin, Zeeshan Hayder, Norberto Garcia-Cairasco, Lars Petersson, Clinton Fookes, Simon Denman, Aileen McGonigal
Country-Scale Cropland Mapping in Data-Scarce Settings Using Deep Learning: A Case Study of Nigeria
Joaquin Gajardo, Michele Volpi, Daniel Onwude, Thijs Defraeye
Catastrophic Forgetting in Deep Learning: A Comprehensive Taxonomy
Everton L. Aleixo, Juan G. Colonna, Marco Cristo, Everlandio Fernandes
One step closer to unbiased aleatoric uncertainty estimation
Wang Zhang, Ziwen Ma, Subhro Das, Tsui-Wei Weng, Alexandre Megretski, Luca Daniel, Lam M. Nguyen
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs
Ruibin Zeng, Minglong Lei, Lingfeng Niu, Lan Cheng
ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks
Pumeng Lyu, Tao Tang, Fenghua Ling, Jing-Jia Luo, Niklas Boers, Wanli Ouyang, Lei Bai
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data
Kang Lin, Reinhard Heckel