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
Deep learning for model correction of dynamical systems with data scarcity
Caroline Tatsuoka, Dongbin Xiu
Leveraging Deep Learning for Time Series Extrinsic Regression in predicting photometric metallicity of Fundamental-mode RR Lyrae Stars
Lorenzo Monti, Tatiana Muraveva, Gisella Clementini, Alessia Garofalo
Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers
Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
BlurryScope: a cost-effective and compact scanning microscope for automated HER2 scoring using deep learning on blurry image data
Michael John Fanous, Christopher Michael Seybold, Hanlong Chen, Nir Pillar, Aydogan Ozcan
Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field
Koustav Ghosal, Arun Singh, Samir Malakar, Shalivahan Srivastava, Deepak Gupta
Efficient Frequency Selective Surface Analysis via End-to-End Model-Based Learning
Cheima Hammami (INSA Rennes, IETR), Lucas Polo-López (IETR, INSA Rennes), Luc Le Magoarou (INSA Rennes, IETR)
Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice
Silin Chen, Ziqian Bi, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Ming Li, Jintao Ren, Qian Niu, Ming Liu
Cutting Through the Confusion and Hype: Understanding the True Potential of Generative AI
Ante Prodan, Jo-An Occhipinti, Rehez Ahlip, Goran Ujdur, Harris A. Eyre, Kyle Goosen, Luke Penza, Mark Heffernan
Cancer Cell Classification using Deep Learning
Praneeth Kumar T, Nidhi Srivastava, Rakshith Mahishi, Chayadevi M L
Systematic Review: Text Processing Algorithms in Machine Learning and Deep Learning for Mental Health Detection on Social Media
Yuchen Cao, Jianglai Dai, Zhongyan Wang, Yeyubei Zhang, Xiaorui Shen, Yunchong Liu, Yexin Tian
Multimodal Flare Forecasting with Deep Learning
Grégoire Francisco, Sabrina Guastavino, Teresa Barata, João Fernandes, Dario Del Moro
Exploring how deep learning decodes anomalous diffusion via Grad-CAM
Jaeyong Bae, Yongjoo Baek, Hawoong Jeong
AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review
Yuki Hagiwara, Octavia-Andreaa Ciora, Maureen Monnet, Gino Lancho, Jeanette Miriam Lorenz
Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt
Edi Sutoyo, Paris Avgeriou, Andrea Capiluppi
Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
Jintao Ren, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Xuanhe Pan, Jinlang Wang, Keyu Chen, Caitlyn Heqi Yin, Pohsun Feng, Yizhu Wen, Tianyang Wang, Silin Chen, Ming Li, Jiawei Xu, Ming Liu
Science Time Series: Deep Learning in Hydrology
Junyang He, Ying-Jung Chen, Anushka Idamekorala, Geoffrey Fox
Stool Recognition for Colorectal Cancer Detection through Deep Learning
Glenda Hui En Tan (1), Goh Xin Ru Karin (2), Shen Bingquan (3) ((1) Carnegie Mellon University, (2) London School of Economics and Political Science, (3) DSO National Laboratories Singapore)
Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data
Yuhao Gong, Yuchen Zhang, Fei Wang, Chi-Han Lee