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
A Survey on Deep Stereo Matching in the Twenties
Fabio Tosi, Luca Bartolomei, Matteo Poggi
Deep-Graph-Sprints: Accelerated Representation Learning in Continuous-Time Dynamic Graphs
Ahmad Naser Eddin, Jacopo Bono, David Aparício, Hugo Ferreira, Pedro Ribeiro, Pedro Bizarro
Stable Weight Updating: A Key to Reliable PDE Solutions Using Deep Learning
A. Noorizadegan, R. Cavoretto, D. L. Young, C. S. Chen
On the power of data augmentation for head pose estimation
Michael Welter
Leveraging Topological Guidance for Improved Knowledge Distillation
Eun Som Jeon, Rahul Khurana, Aishani Pathak, Pavan Turaga
Topological Persistence Guided Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Hongjun Choi, Ankita Shukla, Yuan Wang, Hyunglae Lee, Matthew P. Buman, Pavan Turaga
Real Time Emotion Analysis Using Deep Learning for Education, Entertainment, and Beyond
Abhilash Khuntia, Shubham Kale
Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE)
Artzai Picon, Pablo Galan, Arantza Bereciartua-Perez, Leire Benito-del-Valle
Deep learning architectures for data-driven damage detection in nonlinear dynamic systems
Harrish Joseph, Giuseppe Quaranta, Biagio Carboni, Walter Lacarbonara
reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
Kai Norman Clasen, Leonard Hackel, Tom Burgert, Gencer Sumbul, Begüm Demir, Volker Markl
Probing Perfection: The Relentless Art of Meddling for Pulmonary Airway Segmentation from HRCT via a Human-AI Collaboration Based Active Learning Method
Shiyi Wang, Yang Nan, Sheng Zhang, Federico Felder, Xiaodan Xing, Yingying Fang, Javier Del Ser, Simon L F Walsh, Guang Yang
Accelerated Proton Resonance Frequency-based Magnetic Resonance Thermometry by Optimized Deep Learning Method
Sijie Xu, Shenyan Zong, Chang-Sheng Mei, Guofeng Shen, Yueran Zhao, He Wang
Joint Segmentation and Image Reconstruction with Error Prediction in Photoacoustic Imaging using Deep Learning
Ruibo Shang, Geoffrey P. Luke, Matthew O'Donnell
Deep Learning Based Apparent Diffusion Coefficient Map Generation from Multi-parametric MR Images for Patients with Diffuse Gliomas
Zach Eidex, Mojtaba Safari, Jacob Wynne, Richard L. J. Qiu, Tonghe Wang, David Viar Hernandez, Hui-Kuo Shu, Hui Mao, Xiaofeng Yang
CALICO: Confident Active Learning with Integrated Calibration
Lorenzo S. Querol, Hajime Nagahara, Hideaki Hayashi
Empirical Tests of Optimization Assumptions in Deep Learning
Hoang Tran, Qinzi Zhang, Ashok Cutkosky
Scalable Nested Optimization for Deep Learning
Jonathan Lorraine
Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data
Etienne Chollet, Yaël Balbastre, Chiara Mauri, Caroline Magnain, Bruce Fischl, Hui Wang