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
Semantic segmentation on multi-resolution optical and microwave data using deep learning
Jai G Singla, Bakul Vaghela
EUR/USD Exchange Rate Forecasting incorporating Text Mining Based on Pre-trained Language Models and Deep Learning Methods
Xiangyu Shi, Hongcheng Ding, Salaar Faroog, Deshinta Arrova Dewi, Shamsul Nahar Abdullah, Bahiah A Malek
AdaS&S: a One-Shot Supernet Approach for Automatic Embedding Size Search in Deep Recommender System
He Wei, Yuekui Yang, Yang Zhang, Haiyang Wu, Meixi Liu, Shaoping Ma
Increasing Rosacea Awareness Among Population Using Deep Learning and Statistical Approaches
Chengyu Yang, Chengjun Liu
Permutative redundancy and uncertainty of the objective in deep learning
Vacslav Glukhov
Classification of residential and non-residential buildings based on satellite data using deep learning
Jai G Singla
Methane projections from Canada's oil sands tailings using scientific deep learning reveal significant underestimation
Esha Saha, Oscar Wang, Amit K. Chakraborty, Pablo Venegas Garcia, Russell Milne, Hao Wang
A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra
Raúl Santoveña, Carlos Dafonte, Minia Manteiga
FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher Information
Shreen Gul, Mohamed Elmahallawy, Sanjay Madria, Ardhendu Tripathy
Do Histopathological Foundation Models Eliminate Batch Effects? A Comparative Study
Jonah Kömen, Hannah Marienwald, Jonas Dippel, Julius Hense
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
Mohammad Imran Hossain, Muhammad Zain Amin, Daniel Tweneboah Anyimadu, Taofik Ahmed Suleiman
Trends, Challenges, and Future Directions in Deep Learning for Glaucoma: A Systematic Review
Mahtab Faraji, Homa Rashidisabet, George R. Nahass, RV Paul Chan, Thasarat S Vajaranant, Darvin Yi
Plasticity Loss in Deep Reinforcement Learning: A Survey
Timo Klein, Lukas Miklautz, Kevin Sidak, Claudia Plant, Sebastian Tschiatschek
Rethinking Deep Learning: Non-backpropagation and Non-optimization Machine Learning Approach Using Hebbian Neural Networks
Kei Itoh
Continuous Sign Language Recognition System using Deep Learning with MediaPipe Holistic
Sharvani Srivastava, Sudhakar Singh, Pooja, Shiv Prakash
Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method
Yunfan Zhang, Zekun Niu, Minghui Shi, Weisheng Hu, Lilin Yi
Saliency Assisted Quantization for Neural Networks
Elmira Mousa Rezabeyk, Salar Beigzad, Yasin Hamzavi, Mohsen Bagheritabar, Seyedeh Sogol Mirikhoozani