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
A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning
Wanyu Bian
Tilting the Odds at the Lottery: the Interplay of Overparameterisation and Curricula in Neural Networks
Stefano Sarao Mannelli, Yaraslau Ivashynka, Andrew Saxe, Luca Saglietti
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
An expert-driven data generation pipeline for histological images
Roberto Basla, Loris Giulivi, Luca Magri, Giacomo Boracchi
Distributional Refinement Network: Distributional Forecasting via Deep Learning
Benjamin Avanzi, Eric Dong, Patrick J. Laub, Bernard Wong
Local Methods with Adaptivity via Scaling
Savelii Chezhegov, Sergey Skorik, Nikolas Khachaturov, Danil Shalagin, Aram Avetisyan, Martin Takáč, Yaroslav Kholodov, Aleksandr Beznosikov
A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods
Amir Masoud Rahmani, Parisa Khoshvaght, Hamid Alinejad-Rokny, Samira Sadeghi, Parvaneh Asghari, Zohre Arabi, Mehdi Hosseinzadeh
A Survey of Deep Learning Based Radar and Vision Fusion for 3D Object Detection in Autonomous Driving
Di Wu, Feng Yang, Benlian Xu, Pan Liao, Bo Liu
Effective Data Selection for Seismic Interpretation through Disagreement
Ryan Benkert, Mohit Prabhushankar, Ghassan AlRegib
Research on the Application of Computer Vision Based on Deep Learning in Autonomous Driving Technology
Jingyu Zhang, Jin Cao, Jinghao Chang, Xinjin Li, Houze Liu, Zhenglin Li
Arabic Handwritten Text for Person Biometric Identification: A Deep Learning Approach
Mazen Balat, Youssef Mohamed, Ahmed Heakl, Ahmed Zaky
An Effective Weight Initialization Method for Deep Learning: Application to Satellite Image Classification
Wadii Boulila, Eman Alshanqiti, Ayyub Alzahem, Anis Koubaa, Nabil Mlaiki
A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning
Fahimeh Fakour, Ali Mosleh, Ramin Ramezani
Advancing Ear Biometrics: Enhancing Accuracy and Robustness through Deep Learning
Youssef Mohamed, Zeyad Youssef, Ahmed Heakl, Ahmed Zaky
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Tri Dao, Albert Gu
An Organic Weed Control Prototype using Directed Energy and Deep Learning
Deng Cao, Hongbo Zhang, Rajveer Dhillon
Communication-Efficient Distributed Deep Learning via Federated Dynamic Averaging
Michail Theologitis, Georgios Frangias, Georgios Anestis, Vasilis Samoladas, Antonios Deligiannakis
Searching for internal symbols underlying deep learning
Jung H. Lee, Sujith Vijayan
Deep Learning without Weight Symmetry
Li Ji-An, Marcus K. Benna
Capturing Climatic Variability: Using Deep Learning for Stochastic Downscaling
Kiri Daust, Adam Monahan