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

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends
Convolutional Neural Network Technical Challenge 3D Convolutional Neural Network Deep Learning CNN Architecture Future Trend Different Convolutional Neural Network Direct Convolution Financial Application
Multi-Objective Learning for Deformable Image Registration
Deep Learning Deformable Image Registration Multiple Registration Output Multi Objective Learning Deep Learning Algorithm

February 21, 2024

The Importance of Architecture Choice in Deep Learning for Climate Applications
Atlantic Meridional Overturning Circulation Deep Learning Climate Change Application Climate Scenario Importance Aware Bayesian Neural Network Climate Projection
BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery
Satellite Image Remote Sensing Image Segmentation Based Approach Cloud Segmentation Cloud Classification Cloud Detection Deep Learning
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning
Potential Scalability Deep Learning Gradient Sparsification
Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures
Tensorflow Probability Deep Learning Model Deep Learning CUDA Implementation Green AI Inference Performance Energy Consumption Pytorch Model
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Aggregation Network YOLOv5 Model Gradient Information Deep Learning Intermediate Layer

February 19, 2024

Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
Localization Method Landmark Based SLAM Object Landmark Deep Learning Artificial Landmark Adaptive Sparse Anchor GPS Denied Landmark Detection Stereo Vision
Designing High-Performing Networks for Multi-Scale Computer Vision
Computer Vision Model Deep Learning High Performing Network Computer Vision
An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
Global Evaluation LiDAR Registration Disparity Estimation Deep Learning Stereo Dataset Disparity Map 3D Scene Reconstruction Aerial Image
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
Program Repair Deep Learning Program Reduction Technique
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
Machine Classification Deep Learning Scheduling Algorithm Significant Delay Change Reinforcement Learning Deep Neural Network Optimality Gap
Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training
Adversarial Attack Deep Learning Adversarial Training Adversarial Example Native Robustness Adversarial Feature Alignment
MM-SurvNet: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion
Multimodal Data Fusion TCGA BRCA Dataset Breast Cancer Breast Cancer Treatment Deep Learning

February 18, 2024

Evaluating Adversarial Robustness of Low dose CT Recovery
Adversarial Robustness Low Dose Computed Tomography Deep Learning Deep Network

February 17, 2024

Exploiting T-norms for Deep Learning in Autonomous Driving
P$ Norm Neuro Symbolic Deep Learning Autonomous Driving Deep Learning Model
Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning
Deep Learning Deep Neural Network Topological Data Analysis Unified Framework Shortcut Learning Shortcut Pattern Avoidance Loss Topological Feature

Deep Learning

Papers - Page 77

A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends

Multi-Objective Learning for Deformable Image Registration

Enhancing Power Quality Event Classification with AI Transformer Models

Boosting gets full Attention for Relational Learning

VLPose: Bridging the Domain Gap in Pose Estimation with Language-Vision Tuning

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

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

Evaluating Adversarial Robustness of Low dose CT Recovery

Exploiting T-norms for Deep Learning in Autonomous Driving

Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning