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.
3874papers
Papers - Page 77
February 23, 2024
February 22, 2024
February 21, 2024
The Importance of Architecture Choice in Deep Learning for Climate Applications
BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
February 19, 2024
Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
Designing High-Performing Networks for Multi-Scale Computer Vision
An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training
MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
February 18, 2024