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

October 15, 2024

Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep Learning
Clement Wang, Antoine Debouchage, Valentin Goldité, Aurélien Wery, Jules Salzinger
Deep Learning Deep Neural Network Satellite Data Multispectral Data Vegetation Index Leaf Size
Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep Learning
Rijun Wang, Guanghao Zhang, Hongyang Chen, Xinye Yu, Yesheng Chen, Fulong Liang, Xiangwei Mou, Bo Wang
Deep Learning Development Activity Level Test Bark Removal
Cross-Dataset Generalization in Deep Learning
Xuyu Zhang, Haofan Huang, Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai, Honglin Liu
Deep Learning Strong Generalization Deep Learning Application Cross Dataset Relative Over Generalization

October 14, 2024

Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges
Rami Darwish, Mahmoud Abdelsalam, Sajad Khorsandroo
Deep Learning Comprehensive Survey Comprehensive Taxonomy Malware Detection IOT Technology Malware Family Research Challenge
What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis
Weronika Ormaniec, Felix Dangel, Sidak Pal Singh
Deep Learning Convolutional Neural Network Transformer Based Transformer Architecture DCU Insight AQ Hessian Matrix Theoretical Analysis
Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning
Farhang Yeganegi, Arian Eamaz, Mojtaba Soltanalian
Deep Learning Deep Learning Model Fault Aware
Early Diagnoses of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models
Alaa Awad, Mohamed Hegazy, Salah A. Aly
Deep Learning Deep Learning Model Data Detection State of the Art Deep Look Once Version 8 Early Diagnosis Acute Lymphoblastic Leukemia
Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation
Diogo Reis Santos, Andrea Protani, Lorenzo Giusti, Albert Sund Aillet, Pierpaolo Brutti, Luigi Serio
Deep Learning Data Detection Task Feasibility Atrial Fibrillation Electrocardiogram Data Federated Deep
Do we need more complex representations for structure? A comparison of note duration representation for Music Transformers
Gabriel Souza, Flavio Figueiredo, Alexei Machado, Deborah Guimarães
Deep Learning Consistent Comparison Inner Structure Music Generation Structural Similarity Index Numerical Representation
Comparison of deep learning and conventional methods for disease onset prediction
Luis H. John, Chungsoo Kim, Jan A. Kors, Junhyuk Chang, Hannah Morgan-Cooper, Priya Desai, Chao Pang, Peter R. Rijnbeek, Jenna M. Reps, Egill A. Fridgeirsson
Deep Learning Human Prediction Consistent Comparison Current Method Prediction Performance Prediction Method Healthcare Datasets Early Onset
On Representation of 3D Rotation in the Context of Deep Learning
Viktória Pravdová, Lukáš Gajdošech, Hassan Ali, Viktor Kocur
Deep Learning Deep Neural Network Context Information Individual Representation 6D Object Rotation Estimation 3D Rotation Rotation Representation
Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data
Robert Graf, Florian Hunecke, Soeren Pohl, Matan Atad, Hendrik Moeller, Sophie Starck, Thomas Kroencke, Stefanie Bette, Fabian Bamberg, Tobias Pischon, Thoralf Niendorf, Carsten Schmidt, Johannes C. Paetzold, Daniel Rueckert, Jan S Kirschke
Deep Learning Supervised Autoencoder Jina Embeddings Medical Image Datasets Unseen Data Diffusion Autoencoder Lumbar Spine
Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification
Sadam Hussain, Mansoor Ali, Usman Naseem, Beatriz Alejandra Bosques Palomo, Mario Alexis Monsivais Molina, Jorge Alberto Garza Abdala, Daly Betzabeth Avendano Avalos, Servando Cardona-Huerta, T. Aaron Gulliver, Jose Gerardo Tamez Pena
Deep Learning Transformer Model Multimodal Data Multimodal Dataset Performance Evaluation Breast Cancer Classification Digital Mammogram
Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms
Santiago Pérez, Camila Gómez, Matías Rodríguez
Deep Learning Comparative Study Fire YOLOv5 Attains Obstacle Detection YOLO Model

October 12, 2024

Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health
Abdullah Mamun, Lawrence D. Devoe, Mark I. Evans, David W. Britt, Judith Klein-Seetharaman, Hassan Ghasemzadeh
Deep Learning Greater Public Use Artificial Intelligence Model Risk Factor Neonatal Outcome
Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques
Pohsun Feng, Ziqian Bi, Yizhu Wen, Benji Peng, Junyu Liu, Caitlyn Heqi Yin, Tianyang Wang, Keyu Chen, Sen Zhang, Ming Li, Jiawei Xu, Ming Liu, Xuanhe Pan, Jinlang Wang, Qian Niu
Language Model Deep Learning Machine Learning Big Data Online autoML AutoML Tool Auto Sklearn
Improving 3D Finger Traits Recognition via Generalizable Neural Rendering
Hongbin Xu, Junduan Huang, Yuer Ma, Zifeng Li, Wenxiong Kang
Deep Learning 3D Reconstruction High Quality Rendering 3D Fingerprint

Deep Learning

Papers

Leveraging Multi-Temporal Sentinel 1 and 2 Satellite Data for Leaf Area Index Estimation With Deep Learning

Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep Learning

Cross-Dataset Generalization in Deep Learning

Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges

What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis

Data-Aware Training Quality Monitoring and Certification for Reliable Deep Learning

Early Diagnoses of Acute Lymphoblastic Leukemia Using YOLOv8 and YOLOv11 Deep Learning Models

Feasibility Analysis of Federated Neural Networks for Explainable Detection of Atrial Fibrillation

Do we need more complex representations for structure? A comparison of note duration representation for Music Transformers

Comparison of deep learning and conventional methods for disease onset prediction

On Representation of 3D Rotation in the Context of Deep Learning

Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings using Spine MRI data

Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification

Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms

Towards Reproducible Learning-based Compression

TopOC: Topological Deep Learning for Ovarian and Breast Cancer Diagnosis

Distributed Intelligent Video Surveillance for Early Armed Robbery Detection based on Deep Learning

Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques

Improving 3D Finger Traits Recognition via Generalizable Neural Rendering