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 19, 2023

Benchmarking GPUs on SVBRDF Extractor Model
Narayan Kandel, Melanie Lambert
Deep Learning Single GPU GPU Architecture SVBRDF Estimation
Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning
Jack Breen, Katie Allen, Kieran Zucker, Geoff Hall, Nishant Ravikumar, Nicolas M. Orsi
Deep Learning Multiple Instance Learning Histopathological Image Hierarchical Vision Transformer Ovarian Cancer
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes
Yannik Hahn, Robert Maack, Guido Buchholz, Marion Purrio, Matthias Angerhausen, Hasan Tercan, Tobias Meisen
Deep Learning Quality Assessment Quality Prediction Critical Weld Key Performance Characteristic
A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification
Thanh Phuong Vu, Tien Nhat Nguyen, N. Minh Nhat Hoang, Gia Minh Hoang
Deep Learning Alzheimer'S Disease Disease Classification 3D Convolutional Dual Attention Reproducible Deep Learning Neurodegenerative Disease
Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights
Yichuan Deng, Zhao Song, Shenghao Xie, Chiwun Yang
Deep Learning Transformer Based Loss Function Layer Wise Attention Weight New Wallet Recovery Process
Deep Learning Techniques for Video Instance Segmentation: A Survey
Chenhao Xu, Chang-Tsun Li, Yongjian Hu, Chee Peng Lim, Douglas Creighton
Deep Learning Timely Survey Computer Vision Multi Object Tracking Video Instance Segmentation

October 18, 2023

On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields
Nicola Rares Franco, Daniel Fraulin, Andrea Manzoni, Paolo Zunino
Deep Learning Reduced Order Numerical Experiment Error Bound Order Model Latent Dimension Random Field Deep Autoencoders
Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
Yang Li, Jiting Cao, Yan Xu, Lipeng Zhu, Zhao Yang Dong
Deep Learning Transformer Architecture Class Imbalance Power System Stability Analysis Short Term Voltage Stability

October 17, 2023

Neural Attention: Enhancing QKV Calculation in Self-Attention Mechanism with Neural Networks
Muhan Zhang
Neural Network Deep Learning Self Attention
Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis
Guoxin Wang, Qingyuan Wang, Ganesh Neelakanta Iyer, Avishek Nag, Deepu John
Deep Learning Supervised Autoencoder Unsupervised Learning Masked Autoencoders Unsupervised Pre Training Electrocardiogram Analysis
A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function
Khoa Tran, Lam Pham, Vy-Rin Nguyen, Ho-Si-Hung Nguyen
Deep Learning Loss Function Fault Detection Robust Deep Motor Health Monitoring
Deep Learning based Spatially Dependent Acoustical Properties Recovery
Ruixian Liu, Peter Gerstoft
Deep Learning Physic Informed Neural Network
MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts
Fardin Ahsan Sakib, Ahnaf Atef Choudhury, Ozlem Uzuner
Deep Learning Social Medium Depression Symptom Damo NLP Beck Depression Inventory Depression Severity
Intelligent Software Tooling for Improving Software Development
Nathan Cooper
Deep Learning Software Engineering Image Datasets Intelligent System Software Development Open Source Code

October 16, 2023

Deep learning applied to EEG data with different montages using spatial attention
Dung Truong, Muhammad Abdullah Khalid, Arnaud Delorme
Deep Learning EEG Data Spatial Attention EEG Datasets Coherent Montage
Microscaling Data Formats for Deep Learning
Bita Darvish Rouhani, Ritchie Zhao, Ankit More, Mathew Hall, Alireza Khodamoradi, Summer Deng, Dhruv Choudhary, Marius Cornea, Eric Dellinger, Kristof Denolf, Stosic Dusan, Venmugil Elango, Maximilian Golub, Alexander Heinecke, Phil James-Roxby, Dharmesh Jani, Gaurav Kolhe, Martin Langhammer, Ada Li, Levi Melnick, Maral Mesmakhosroshahi, Andres Rodriguez, Michael Schulte, Rasoul Shafipour, Lei Shao, Michael Siu, Pradeep Dubey, Paulius Micikevicius, Maxim Naumov, Colin Verrilli, Ralph Wittig, Doug Burger, Eric Chung
Deep Learning Deep Learning Application Data Format
Machine learning in physics: a short guide
Francisco A. Rodrigues
Reinforcement Learning Deep Learning Machine Learning Symbolic Regression Theoretical Physic Practical Guide
Generalizing Medical Image Representations via Quaternion Wavelet Networks
Luigi Sigillo, Eleonora Grassucci, Aurelio Uncini, Danilo Comminiello
Deep Learning Representation Learning Medical Image Analysis Quaternion Valued
An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
Fabio Azzalini, Tommaso Dolci, Marco Vagaggini
Deep Learning Electronic Health Record Interpretable Deep Learning Hospital Readmission Unplanned Readmission Hospital Readmission Prediction
Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation
Shisheng Zhang, Ramtin Gharleghi, Sonit Singh, Arcot Sowmya, Susann Beier
Deep Learning Convolutional Neural Network Encoder Decoder Coronary Artery Segmentation

Deep Learning

Papers

Benchmarking GPUs on SVBRDF Extractor Model

Predicting Ovarian Cancer Treatment Response in Histopathology using Hierarchical Vision Transformers and Multiple Instance Learning

Towards a Deep Learning-based Online Quality Prediction System for Welding Processes

A reproducible 3D convolutional neural network with dual attention module (3D-DAM) for Alzheimer's disease classification

Unmasking Transformers: A Theoretical Approach to Data Recovery via Attention Weights

Deep Learning Techniques for Video Instance Segmentation: A Survey

On the latent dimension of deep autoencoders for reduced order modeling of PDEs parametrized by random fields

Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance

Neural Attention: Enhancing QKV Calculation in Self-Attention Mechanism with Neural Networks

Unsupervised Pre-Training Using Masked Autoencoders for ECG Analysis

A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function

Deep Learning based Spatially Dependent Acoustical Properties Recovery

MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts

Intelligent Software Tooling for Improving Software Development

Deep learning applied to EEG data with different montages using spatial attention

Microscaling Data Formats for Deep Learning

Machine learning in physics: a short guide

Generalizing Medical Image Representations via Quaternion Wavelet Networks

An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records

Assessing Encoder-Decoder Architectures for Robust Coronary Artery Segmentation