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
The Importance of Architecture Choice in Deep Learning for Climate Applications
Simon Dräger, Maike Sonnewald
BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
Loddo Fabio, Dario Piga, Michelucci Umberto, El Ghazouali Safouane
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
Daegun Yoon, Sangyoon Oh
Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Negar Alizadeh, Fernando Castor
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao
Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
Ganesh Sapkota, Sanjay Madria
Designing High-Performing Networks for Multi-Scale Computer Vision
Cédric Picron
An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
Teng Wu, Bruno Vallet, Marc Pierrot-Deseilligny, Ewelina Rupnik
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin Vechev
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
Michal Bouška, Přemysl Šůcha, Antonín Novák, Zdeněk Hanzálek
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training
Leo Hyun Park, Jaeuk Kim, Myung Gyo Oh, Jaewoo Park, Taekyoung Kwon
MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
Raktim Kumar Mondol, Ewan K. A. Millar, Arcot Sowmya, Erik Meijering
DeepSRGM -- Sequence Classification and Ranking in Indian Classical Music with Deep Learning
Sathwik Tejaswi Madhusudhan, Girish Chowdhary
A chaotic maps-based privacy-preserving distributed deep learning for incomplete and Non-IID datasets
Irina Arévalo, Jose L. Salmeron
NYCTALE: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction
Sadaf Khademi, Anastasia Oikonomou, Konstantinos N. Plataniotis, Arash Mohammadi