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

November 30, 2023

Reconstructing Historical Climate Fields With Deep Learning
Nils Bochow, Anna Poltronieri, Martin Rypdal, Niklas Boers
Deep Learning Earth System Fast Fourier Convolution Climate Model Output
Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans
Long Chen, Liben Chen, Binfeng Xu, Wenxin Zhang, Narges Razavian
Deep Learning Deep Learning Model Alzheimer'S Disease Automatic Detection Common Disease Early Detection Multi Modal Fusion Mild Cognitive Impairment Magnetic Resonance Imaging Data

November 29, 2023

Towards Real-World Focus Stacking with Deep Learning
Alexandre Araujo, Jean Ponce, Julien Mairal
Deep Learning Image Fusion Light Field Camera Focus Image Focal Stack
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri
Deep Learning Timely Survey Design Principle Heterogeneous Computing Deep Learning Accelerator Heterogeneous Architecture
Transformer Based Model for Predicting Rapid Impact Compaction Outcomes: A Case Study of Utapao International Airport
Sompote Youwai, Sirasak Detcheewa
Deep Learning Case Study Transformer Architecture Grounding Network International Airport

November 28, 2023

Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations
Maximilian Dreyer, Reduan Achtibat, Wojciech Samek, Sebastian Lapuschkin
Deep Learning Deep Neural Network Explainable AI XAI Method Opaque Machine Learning Prototypical Representation XAI Based
Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data
Gonzalo Uribarri, Simon Ekman von Huth, Josefine Waldthaler, Per Svenningsson, Erik Fransén
Deep Learning Time Series Classification Parkinson Disease Eye Tracking Eye Tracking Data Eye Movement Fixation Prediction

November 27, 2023

Cross Entropy in Deep Learning of Classifiers Is Unnecessary -- ISBE Error is All You Need
Wladyslaw Skarbek
Deep Learning Back Propagation Simple Classifier Softmax Function Error Feedback Cross Entropy Deep Learning Classifier
Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI
Arda Pekis, Vignesh Kannan, Evandros Kaklamanos, Anu Antony, Snehal Patel, Tyler Earnest
Deep Learning Tumor Segmentation Object Localization 3D Semantic Segmentation Tumor Cell Tissue Context Breast MRI 3D Tumor
RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture
Jingdi Chen, Lei Zhang, Joseph Riem, Gina Adam, Nathaniel D. Bastian, Tian Lan
Deep Learning Deep Neural Network Network Intrusion Detection Explainable Machine Learning Memristive Device Packet Level Integrated Development Environment
Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning
Alireza Bagheri Rajeoni, Breanna Pederson, Daniel G. Clair, Susan M. Lessner, Homayoun Valafar
Deep Learning Automatic Measurement Aortic Calcification
Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback
Xiangyi Li, Jiajia Guo, Chao-Kai Wen, Shi Jin
Deep Learning Neural Architecture Search Neural Architecture Neural Network Architecture
Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning
Michael Adjeisah, Kwame Omono Asamoah, Martha Asamoah Yeboah, Raji Rafiu King, Godwin Ferguson Achaab, Kingsley Adjei
Deep Learning Artificial Intelligence Deep Learning Model Classical Machine Learning
Event Detection in Time Series: Universal Deep Learning Approach
Menouar Azib, Benjamin Renard, Philippe Garnier, Vincent Génot, Nicolas André
Deep Learning Time Series Binary Classification Event Detection Deep Regression Universal Deep Based Event
Global $\mathcal{L}^2$ minimization at uniform exponential rate via geometrically adapted gradient descent in Deep Learning
Thomas Chen
Deep Learning Gradient Descent World Event Gradient Flow Exponential Mechanism Differential Geometry Sub Riemannian

November 26, 2023

ChatGPT Application In Summarizing An Evolution Of Deep Learning Techniques In Imaging: A Qualitative Study
Arman Sarraf, Amirabbas Abbaspour
Deep Learning Natural Language Processing ChatGPT Generated Conversation Structured Summary Text Summarization Imaging Technique Qualitative Analysis Summarization Ability
Machine-Generated Text Detection using Deep Learning
Raghav Gaggar, Ashish Bhagchandani, Harsh Oza
Deep Learning Detection Method Machine Generated Text Twitter Sentiment
Applying statistical learning theory to deep learning
Cédric Gerbelot, Avetik Karagulyan, Stefani Karp, Kavya Ravichandran, Menachem Stern, Nathan Srebro
Deep Learning Gradient Descent Inductive Bias Mirror Descent Statistical Learning Theory Learning Theory
A convergence result of a continuous model of deep learning via \L{}ojasiewicz--Simon inequality
Noboru Isobe
Deep Learning Loss Function Convergence Analysis Wasserstein Gradient Flow Non Convex Objective Continuous Model
ASI: Accuracy-Stability Index for Evaluating Deep Learning Models
Wei Dai, Daniel Berleant
Deep Learning Deep Learning Model Accuracy Measurement

Deep Learning

Papers

Reconstructing Historical Climate Fields With Deep Learning

Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans

Towards Real-World Focus Stacking with Deep Learning

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Transformer Based Model for Predicting Rapid Impact Compaction Outcomes: A Case Study of Utapao International Airport

Understanding the (Extra-)Ordinary: Validating Deep Model Decisions with Prototypical Concept-based Explanations

Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data

Cross Entropy in Deep Learning of Classifiers Is Unnecessary -- ISBE Error is All You Need

Seeing Beyond Cancer: Multi-Institutional Validation of Object Localization and 3D Semantic Segmentation using Deep Learning for Breast MRI

RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture

Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning

Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback

Adinkra Symbol Recognition using Classical Machine Learning and Deep Learning

Event Detection in Time Series: Universal Deep Learning Approach

Global $\mathcal{L}^2$ minimization at uniform exponential rate via geometrically adapted gradient descent in Deep Learning

ChatGPT Application In Summarizing An Evolution Of Deep Learning Techniques In Imaging: A Qualitative Study

Machine-Generated Text Detection using Deep Learning

Applying statistical learning theory to deep learning

A convergence result of a continuous model of deep learning via \L{}ojasiewicz--Simon inequality

ASI: Accuracy-Stability Index for Evaluating Deep Learning Models