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
Why Warmup the Learning Rate? Underlying Mechanisms and Improvements
Dayal Singh Kalra, Maissam Barkeshli
Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure
Can Pouliquen, Mathurin Massias, Titouan Vayer
Tool Wear Prediction in CNC Turning Operations using Ultrasonic Microphone Arrays and CNNs
Jan Steckel, Arne Aerts, Erik Verreycken, Dennis Laurijssen, Walter Daems
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning
Yuxiang Hu, Jinxin Hu, Ting Xu, Bo Zhang, Jiajie Yuan, Haozhang Deng
Opportunities in deep learning methods development for computational biology
Alex Jihun Lee, Reza Abbasi-Asl
Deep Learning Based Joint Multi-User MISO Power Allocation and Beamforming Design
Cemil Vahapoglu, Timothy J. O'Shea, Tamoghna Roy, Sennur Ulukus
Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey
Feng Liang, Zhen Zhang, Haifeng Lu, Chengming Li, Victor C. M. Leung, Yanyi Guo, Xiping Hu
Deep Learning for Slum Mapping in Remote Sensing Images: A Meta-analysis and Review
Anjali Raj, Adway Mitra, Manjira Sinha
Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning
Yidong Zhu, Nadia B Aimandi, Mohammad Arif Ul Alam
A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Models
Yao Lu, Yutao Zhu, Yuqi Li, Dongwei Xu, Yun Lin, Qi Xuan, Xiaoniu Yang
Automated Pavement Cracks Detection and Classification Using Deep Learning
Selvia Nafaa, Hafsa Essam, Karim Ashour, Doaa Emad, Rana Mohamed, Mohammed Elhenawy, Huthaifa I. Ashqar, Abdallah A. Hassan, Taqwa I. Alhadidi
GemNet: Menu-Based, Strategy-Proof Multi-Bidder Auctions Through Deep Learning
Tonghan Wang, Yanchen Jiang, David C. Parkes
Deep Implicit Optimization enables Robust Learnable Features for Deformable Image Registration
Rohit Jena, Pratik Chaudhari, James C. Gee
Minimizing Energy Costs in Deep Learning Model Training: The Gaussian Sampling Approach
Challapalli Phanindra Revanth, Sumohana S. Channappayya, C Krishna Mohan
Large-Scale Contextual Market Equilibrium Computation through Deep Learning
Yunxuan Ma, Yide Bian, Hao Xu, Weitao Yang, Jingshu Zhao, Zhijian Duan, Feng Wang, Xiaotie Deng
Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Under Dynamic Lighting and Weather Conditions Using Electric Vehicles
Michael Khalfin, Jack Volgren, Matthew Jones, Luke LeGoullon, Joshua Siegel, Chan-Jin Chung
Cascading Unknown Detection with Known Classification for Open Set Recognition
Daniel Brignac, Abhijit Mahalanobis
Beyond Trend Following: Deep Learning for Market Trend Prediction
Fernando Berzal, Alberto Garcia
Black carbon plumes from gas flaring in North Africa identified from multi-spectral imagery with deep learning
Tuel Alexandre, Kerdreux Thomas, Thiry Louis